2025-03-14T04:04:22.5826027Z Current runner version: '2.322.0'
2025-03-14T04:04:22.5833183Z Runner name: 'i-01e2d8c57795e0761'
2025-03-14T04:04:22.5834082Z Runner group name: 'Default'
2025-03-14T04:04:22.5835064Z Machine name: 'ip-10-0-66-204'
2025-03-14T04:04:22.5839993Z ##[group]GITHUB_TOKEN Permissions
2025-03-14T04:04:22.5843061Z Actions: read
2025-03-14T04:04:22.5843731Z Attestations: read
2025-03-14T04:04:22.5844490Z Checks: read
2025-03-14T04:04:22.5845122Z Contents: read
2025-03-14T04:04:22.5845805Z Deployments: read
2025-03-14T04:04:22.5846497Z Discussions: read
2025-03-14T04:04:22.5847148Z Issues: read
2025-03-14T04:04:22.5847877Z Metadata: read
2025-03-14T04:04:22.5848479Z Packages: read
2025-03-14T04:04:22.5849340Z Pages: read
2025-03-14T04:04:22.5850031Z PullRequests: read
2025-03-14T04:04:22.5850717Z RepositoryProjects: read
2025-03-14T04:04:22.5851422Z SecurityEvents: read
2025-03-14T04:04:22.5852150Z Statuses: read
2025-03-14T04:04:22.5852784Z ##[endgroup]
2025-03-14T04:04:22.5856657Z Secret source: Actions
2025-03-14T04:04:22.5857839Z Prepare workflow directory
2025-03-14T04:04:22.8825303Z Prepare all required actions
2025-03-14T04:04:22.8869365Z Getting action download info
2025-03-14T04:04:23.0702148Z Download action repository 'pytorch/test-infra@main' (SHA:de00dac6adc071cb2f9861380a0ed3947b93e5cc)
2025-03-14T04:04:24.4492540Z Download action repository 'pytorch/pytorch@main' (SHA:aed0b7a742a2d7b7901790622829cbd2135049a4)
2025-03-14T04:04:37.7613576Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0)
2025-03-14T04:04:37.9571264Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2025-03-14T04:04:38.2113337Z Getting action download info
2025-03-14T04:04:38.3417082Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683)
2025-03-14T04:04:38.5672907Z Getting action download info
2025-03-14T04:04:38.6670009Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e)
2025-03-14T04:04:38.8550505Z Getting action download info
2025-03-14T04:04:38.9589817Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2025-03-14T04:04:39.0961067Z Getting action download info
2025-03-14T04:04:39.2308837Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (aed0b7a742a2d7b7901790622829cbd2135049a4)
2025-03-14T04:04:39.2310951Z ##[group] Inputs
2025-03-14T04:04:39.2311330Z   build-environment: linux-focal-py3.13-clang10
2025-03-14T04:04:39.2314224Z   test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}
2025-03-14T04:04:39.2317428Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:04:39.2318199Z   sync-tag: 
2025-03-14T04:04:39.2319005Z   timeout-minutes: 600
2025-03-14T04:04:39.2319404Z   use-gha: 
2025-03-14T04:04:39.2319659Z   dashboard-tag: 
2025-03-14T04:04:39.2319938Z   s3-bucket: gha-artifacts
2025-03-14T04:04:39.2320245Z   aws-role-to-assume: 
2025-03-14T04:04:39.2320875Z   disable-monitor: false
2025-03-14T04:04:39.2321449Z ##[endgroup]
2025-03-14T04:04:39.2322005Z Complete job name: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:04:39.2795584Z A job started hook has been configured by the self-hosted runner administrator
2025-03-14T04:04:39.2894724Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh'
2025-03-14T04:04:39.2903862Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:04:39.2904585Z ##[endgroup]
2025-03-14T04:04:40.9710801Z Runner Type: lf.linux.4xlarge
2025-03-14T04:04:40.9711376Z Instance Type: c5.4xlarge
2025-03-14T04:04:40.9711677Z AMI Name: unknown
2025-03-14T04:04:40.9735777Z AMI ID: ami-05b10e08d247fb927
2025-03-14T04:04:46.4605393Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2025-03-14T04:04:46.4605857Z with:
2025-03-14T04:04:46.4606678Z   github-secret: ***
2025-03-14T04:04:46.4607413Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2025-03-14T04:04:46.4608226Z   activate-with-label: false
2025-03-14T04:04:46.4608511Z   label: with-ssh
2025-03-14T04:04:46.4608759Z   remove-existing-keys: true
2025-03-14T04:04:46.4609040Z   fail-silently: true
2025-03-14T04:04:46.4609283Z env:
2025-03-14T04:04:46.4609494Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:04:46.4609766Z ##[endgroup]
2025-03-14T04:04:46.5777477Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info.
2025-03-14T04:04:46.5779214Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys
2025-03-14T04:04:46.5936836Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main
2025-03-14T04:04:46.5937315Z with:
2025-03-14T04:04:46.5937576Z   no-sudo: true
2025-03-14T04:04:46.5937845Z   submodules: recursive
2025-03-14T04:04:46.5938142Z   fetch-depth: 0
2025-03-14T04:04:46.5938409Z env:
2025-03-14T04:04:46.5938652Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:04:46.5938946Z ##[endgroup]
2025-03-14T04:04:46.6017799Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:04:46.6018800Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:04:46.6028383Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:04:46.6028821Z env:
2025-03-14T04:04:46.6029108Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:04:46.6029441Z ##[endgroup]
2025-03-14T04:04:46.6115517Z ##[group]Run # Use all available CPUs for fetching
2025-03-14T04:04:46.6116000Z # Use all available CPUs for fetching
2025-03-14T04:04:46.6116388Z cd "${GITHUB_WORKSPACE}"
2025-03-14T04:04:46.6116765Z git config --global fetch.parallel 0
2025-03-14T04:04:46.6117193Z git config --global submodule.fetchJobs 0
2025-03-14T04:04:46.6117570Z 
2025-03-14T04:04:46.6117967Z # Clean workspace. The default checkout action should also do this, but
2025-03-14T04:04:46.6118490Z # do it here as well just in case
2025-03-14T04:04:46.6118828Z if [[ -d .git ]]; then
2025-03-14T04:04:46.6119247Z   if [ -z "${NO_SUDO}" ]; then
2025-03-14T04:04:46.6119598Z     sudo git clean -ffdx
2025-03-14T04:04:46.6119915Z   else
2025-03-14T04:04:46.6120186Z     git clean -ffdx
2025-03-14T04:04:46.6120489Z   fi
2025-03-14T04:04:46.6120737Z fi
2025-03-14T04:04:46.6126187Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:04:46.6126602Z env:
2025-03-14T04:04:46.6126851Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:04:46.6127147Z   NO_SUDO: true
2025-03-14T04:04:46.6127402Z ##[endgroup]
2025-03-14T04:04:46.9879793Z Removing .additional_ci_files/
2025-03-14T04:04:46.9880199Z Removing .ci/pytorch/fake_numpy/__pycache__/
2025-03-14T04:04:46.9880580Z Removing .pytest_cache/
2025-03-14T04:04:46.9880862Z Removing build/
2025-03-14T04:04:46.9881446Z Removing dist/
2025-03-14T04:04:46.9881820Z Removing logs-test-default-1-6-lf.linux.4xlarge_38752183081.zip
2025-03-14T04:04:46.9882405Z Removing test-jsons-test-default-1-6-lf.linux.4xlarge_38752183081.zip
2025-03-14T04:04:46.9883032Z Removing test-reports-test-default-1-6-lf.linux.4xlarge_38752183081.zip
2025-03-14T04:04:46.9883508Z Removing test-reports/
2025-03-14T04:04:46.9883831Z Removing test/.pytorch-disabled-tests.json
2025-03-14T04:04:46.9884198Z Removing test/__pycache__/
2025-03-14T04:04:46.9884513Z Removing test/autograd/__pycache__/
2025-03-14T04:04:46.9884870Z Removing test/cpp/api/mnist/
2025-03-14T04:04:46.9885250Z Removing test/cpp_api_parity/__pycache__/
2025-03-14T04:04:46.9885719Z Removing test/cpp_extensions/open_registration_extension/build/
2025-03-14T04:04:46.9886275Z Removing test/cpp_extensions/open_registration_extension/install/
2025-03-14T04:04:46.9886923Z Removing test/cpp_extensions/open_registration_extension/pytorch_openreg.egg-info/
2025-03-14T04:04:46.9887466Z Removing test/inductor/__pycache__/
2025-03-14T04:04:46.9887803Z Removing test/test-reports/
2025-03-14T04:04:46.9888129Z Removing test/torch_np/__pycache__/
2025-03-14T04:04:46.9888473Z Removing tools/__pycache__/
2025-03-14T04:04:46.9888786Z Removing tools/stats/__pycache__/
2025-03-14T04:04:46.9889192Z Removing tools/stats/upload_utilization_stats/__pycache__/
2025-03-14T04:04:46.9889619Z Removing tools/testing/__pycache__/
2025-03-14T04:04:46.9890028Z Removing tools/testing/target_determination/__pycache__/
2025-03-14T04:04:46.9890708Z Removing tools/testing/target_determination/heuristics/__pycache__/
2025-03-14T04:04:46.9891160Z Removing usage_log.txt
2025-03-14T04:04:46.9960525Z ##[group]Run actions/checkout@v4
2025-03-14T04:04:46.9960849Z with:
2025-03-14T04:04:46.9961122Z   ref: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:04:46.9961481Z   fetch-depth: 0
2025-03-14T04:04:46.9961753Z   submodules: recursive
2025-03-14T04:04:46.9962039Z   show-progress: false
2025-03-14T04:04:46.9962423Z   repository: pytorch/pytorch
2025-03-14T04:04:46.9962892Z   token: ***
2025-03-14T04:04:46.9963140Z   ssh-strict: true
2025-03-14T04:04:46.9963400Z   ssh-user: git
2025-03-14T04:04:46.9963671Z   persist-credentials: true
2025-03-14T04:04:46.9963968Z   clean: true
2025-03-14T04:04:46.9964242Z   sparse-checkout-cone-mode: true
2025-03-14T04:04:46.9964570Z   fetch-tags: false
2025-03-14T04:04:46.9964830Z   lfs: false
2025-03-14T04:04:46.9965076Z   set-safe-directory: true
2025-03-14T04:04:46.9965373Z env:
2025-03-14T04:04:46.9965612Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:04:46.9965909Z ##[endgroup]
2025-03-14T04:04:47.1057592Z Syncing repository: pytorch/pytorch
2025-03-14T04:04:47.1059065Z ##[group]Getting Git version info
2025-03-14T04:04:47.1059598Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2025-03-14T04:04:47.1060336Z [command]/usr/bin/git version
2025-03-14T04:04:47.1060652Z git version 2.47.1
2025-03-14T04:04:47.1070800Z ##[endgroup]
2025-03-14T04:04:47.1079784Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/6d61a7ca-1ffb-40d6-8961-97a2e84b63b4/.gitconfig'
2025-03-14T04:04:47.1094033Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/6d61a7ca-1ffb-40d6-8961-97a2e84b63b4' before making global git config changes
2025-03-14T04:04:47.1095055Z Adding repository directory to the temporary git global config as a safe directory
2025-03-14T04:04:47.1098311Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:04:47.1134244Z [command]/usr/bin/git config --local --get remote.origin.url
2025-03-14T04:04:47.1152832Z https://github.com/pytorch/pytorch
2025-03-14T04:04:47.1166512Z ##[group]Removing previously created refs, to avoid conflicts
2025-03-14T04:04:47.1169339Z [command]/usr/bin/git rev-parse --symbolic-full-name --verify --quiet HEAD
2025-03-14T04:04:47.1187487Z HEAD
2025-03-14T04:04:47.1220822Z ##[endgroup]
2025-03-14T04:04:47.1223622Z [command]/usr/bin/git submodule status
2025-03-14T04:04:47.1530214Z  7e1e1fe3858c63c251c637ae41a20de425dde96f android/libs/fbjni (v0.1.0-12-g7e1e1fe)
2025-03-14T04:04:47.1611985Z  4dfe081cf6bcd15db339cf2680b9281b8451eeb3 third_party/FP16 (4dfe081)
2025-03-14T04:04:47.1682136Z  b408327ac2a15ec3e43352421954f5b1967701d1 third_party/FXdiv (b408327)
2025-03-14T04:04:47.1767959Z  c07e3a0400713d546e0dea2d5466dd22ea389c73 third_party/NNPACK (c07e3a0)
2025-03-14T04:04:47.1790848Z  e170594ac7cf1dac584da473d4ca9301087090c1 third_party/NVTX (v3.1.0)
2025-03-14T04:04:47.1872906Z  a6bfc237255a6bac1513f7c1ebde6d8aed6b5191 third_party/VulkanMemoryAllocator (v2.1.0-705-ga6bfc23)
2025-03-14T04:04:47.2420685Z  51a0103656eff6fc9bfd39a4597923c4b542c883 third_party/XNNPACK (remotes/origin/ds/ndk-1243-g51a010365)
2025-03-14T04:04:47.2446561Z  0d98dba29d66e93259db7daa53a9327df767a415 third_party/benchmark (v1.6.1)
2025-03-14T04:04:47.2476941Z  8086bbe3a78d931eb96fe12fdc014082e18d18d3 third_party/composable_kernel (mock-tag-test-6-g8086bbe3a)
2025-03-14T04:04:47.2621266Z  3b6597bba913d51161383657829b7e644e59c006 third_party/cpp-httplib (v0.15.3-20-g3b6597b)
2025-03-14T04:04:47.2737037Z  1e83a2fdd3102f65c6f1fb602c1b320486218a99 third_party/cpuinfo (1e83a2f)
2025-03-14T04:04:47.2772318Z  91b7532f3386768bba4f444ee7672b497f34da8a third_party/cudnn_frontend (v0.5-44-g91b7532)
2025-03-14T04:04:47.2873730Z  afa1772203677c5118fcd82537a9c8fefbcc7008 third_party/cutlass (v3.8.0)
2025-03-14T04:04:47.3626674Z  3147391d946bb4b6c68edd901f2add6ac1f31f8c third_party/eigen (3.4.0)
2025-03-14T04:04:47.3989150Z  dbc3157bf256f1339b3fa1fef2be89ac4078be0e third_party/fbgemm (v0.4.1-446-gdbc3157b)
2025-03-14T04:04:47.4075382Z  979702c87a8713a8e0a5e9fee122b90d2ef13be5 third_party/flash-attention (v2.7.4)
2025-03-14T04:04:47.4099718Z  01834de25e4bf3975a9a00e816292b1ad0fe184b third_party/flatbuffers (v23.3.3)
2025-03-14T04:04:47.4532186Z  123913715afeb8a437e6388b4473fcc4753e1c9a third_party/fmt (11.1.4)
2025-03-14T04:04:47.4651710Z  3fb5c176c17c765a3492cd2f0321b0dab712f350 third_party/gemmlowp/gemmlowp (remotes/origin/revert-87-master-135-g3fb5c17)
2025-03-14T04:04:47.4769372Z  5354032ea08eadd7fc4456477f7f7c6308818509 third_party/gloo (5354032)
2025-03-14T04:04:47.5032000Z  b514bdc898e2951020cbdca1304b75f5950d1f59 third_party/googletest (release-1.8.0-3484-gb514bdc8)
2025-03-14T04:04:47.5113022Z  719d8e6cd7f7a0e01b155657526d693acf97c2b3 third_party/ideep (pytorch-rls-v3.7.1)
2025-03-14T04:04:47.5172518Z  5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42 third_party/ittapi (v3.23.0-14-g5b8a7d7)
2025-03-14T04:04:47.5434854Z  2859721fd9e73d3ca1c56f827dbc64e6d68f78a2 third_party/kineto (remotes/origin/sraikund/test-53-g2859721)
2025-03-14T04:04:47.5459922Z  ef685a13cfbe8d418aa2ed34350e21e4938358b6 third_party/kleidiai (v1.3.0)
2025-03-14T04:04:47.5483119Z  b66e3214d8a104669c2ec05ae91ebc26a8f5ab78 third_party/mimalloc (v1.8.2)
2025-03-14T04:04:47.6006583Z  87cda1d6646592ac5866dc703c8e1839046a6806 third_party/nlohmann (v3.10.1-113-g87cda1d6)
2025-03-14T04:04:47.6315570Z  b8baa8446686496da4cc8fda09f2b6fe65c2a02c third_party/onnx (v1.17.0)
2025-03-14T04:04:47.6340958Z  a799f4aed9c94b765dcdaabaeab7d5e7e2310878 third_party/opentelemetry-cpp (v1.14.2)
2025-03-14T04:04:47.6367213Z  9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe third_party/pocketfft (release_for_eigen-11-g9d3ab05)
2025-03-14T04:04:47.6784439Z  d1eca4e4b421cd2997495c4b4e65cea6be4e9b8a third_party/protobuf (v3.7.0-rc.2-1279-gd1eca4e4b)
2025-03-14T04:04:47.6858431Z  072586a71b55b7f8c584153d223e95687148a900 third_party/psimd (heads/master)
2025-03-14T04:04:47.6912289Z  4fe0e1e183925bf8cfa6aae24237e724a96479b8 third_party/pthreadpool (0.1-144-g4fe0e1e)
2025-03-14T04:04:47.6962584Z  a2e59f0e7065404b44dfe92a28aca47ba1378dc4 third_party/pybind11 (v2.11.0-182-ga2e59f0e)
2025-03-14T04:04:47.7031527Z  f45429b087dd7d5bc78bb40dc7cf06425c252d67 third_party/python-peachpy (remotes/origin/pre-generated)
2025-03-14T04:04:47.7142232Z  56e1f79cb140fb9326d612d0be06b5250565cade third_party/sleef (3.7-33-g56e1f79)
2025-03-14T04:04:47.7206885Z  52791a2fd214b2a9dc5759d36725909c1daa7f2e third_party/tensorpipe (remotes/origin/master)
2025-03-14T04:04:47.7219805Z ##[group]Cleaning the repository
2025-03-14T04:04:47.7223160Z [command]/usr/bin/git clean -ffdx
2025-03-14T04:04:47.7413243Z [command]/usr/bin/git reset --hard HEAD
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2025-03-14T04:04:49.0984809Z Updating files: 100% (16541/16541), done.
2025-03-14T04:04:49.1154939Z HEAD is now at bd72c961cb2 Remove temp changes
2025-03-14T04:04:49.1232760Z ##[endgroup]
2025-03-14T04:04:49.1234358Z ##[group]Disabling automatic garbage collection
2025-03-14T04:04:49.1239795Z [command]/usr/bin/git config --local gc.auto 0
2025-03-14T04:04:49.1267598Z ##[endgroup]
2025-03-14T04:04:49.1268058Z ##[group]Setting up auth
2025-03-14T04:04:49.1273869Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-14T04:04:49.1300448Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
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2025-03-14T04:04:49.5415931Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-14T04:04:49.5440775Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
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2025-03-14T04:04:49.9619485Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-14T04:04:49.9635789Z ##[endgroup]
2025-03-14T04:04:49.9636372Z ##[group]Fetching the repository
2025-03-14T04:04:49.9643416Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2025-03-14T04:04:53.3417408Z From https://github.com/pytorch/pytorch
2025-03-14T04:04:53.3417989Z  - [deleted]                 (none)                  -> ciflow/inductor/149174
2025-03-14T04:04:53.3843248Z  - [deleted]                 (none)                  -> ciflow/mps/149174
2025-03-14T04:04:53.3845731Z  - [deleted]                 (none)                  -> ciflow/trunk/149166
2025-03-14T04:04:54.8092485Z    8636b394e08..9db33f3cda6  gh/malfet/227/base      -> origin/gh/malfet/227/base
2025-03-14T04:04:54.8100906Z    259919aeab3..4b1f10d8c28  gh/malfet/227/head      -> origin/gh/malfet/227/head
2025-03-14T04:04:54.8107722Z  + 4c129ff8a05...13ad80715a8 gh/malfet/227/orig      -> origin/gh/malfet/227/orig  (forced update)
2025-03-14T04:04:54.8129544Z    c179971bfcd..aed0b7a742a  main                    -> origin/main
2025-03-14T04:04:54.8141371Z  + 277cb23fd22...c4b8be3d09f update_submodule_FBGEMM -> origin/update_submodule_FBGEMM  (forced update)
2025-03-14T04:04:54.8147847Z    4098a229a04..49570cb4024  viable/strict           -> origin/viable/strict
2025-03-14T04:04:54.8155034Z    1579a02c823..3ffd7552df1  wdvr/iss145259_alt      -> origin/wdvr/iss145259_alt
2025-03-14T04:04:54.8163158Z  * [new tag]                 ciflow/inductor/149176  -> ciflow/inductor/149176
2025-03-14T04:04:54.8164810Z  t [tag update]              ciflow/mps/149123       -> ciflow/mps/149123
2025-03-14T04:04:54.8168895Z  * [new tag]                 ciflow/trunk/146289     -> ciflow/trunk/146289
2025-03-14T04:04:54.8170728Z  * [new tag]                 ciflow/trunk/149064     -> ciflow/trunk/149064
2025-03-14T04:04:54.8688493Z [command]/usr/bin/git rev-parse --verify --quiet aed0b7a742a2d7b7901790622829cbd2135049a4^{object}
2025-03-14T04:04:54.8711819Z aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:04:54.8715544Z ##[endgroup]
2025-03-14T04:04:54.8716058Z ##[group]Determining the checkout info
2025-03-14T04:04:54.8716918Z ##[endgroup]
2025-03-14T04:04:54.8721081Z [command]/usr/bin/git sparse-checkout disable
2025-03-14T04:04:54.9323790Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig
2025-03-14T04:04:54.9350569Z ##[group]Checking out the ref
2025-03-14T04:04:54.9354031Z [command]/usr/bin/git checkout --progress --force aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:04:55.0612966Z Previous HEAD position was bd72c961cb2 Remove temp changes
2025-03-14T04:04:55.0622077Z HEAD is now at aed0b7a742a [c10d] Add param recording for uniqueID broadcasting and allgather (#149166)
2025-03-14T04:04:55.0647385Z ##[endgroup]
2025-03-14T04:04:55.0647860Z ##[group]Setting up auth for fetching submodules
2025-03-14T04:04:55.0654183Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-14T04:04:55.0692204Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2025-03-14T04:04:55.0717570Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2025-03-14T04:04:55.0742575Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2025-03-14T04:04:55.0764768Z ##[endgroup]
2025-03-14T04:04:55.0765223Z ##[group]Fetching submodules
2025-03-14T04:04:55.0767799Z [command]/usr/bin/git submodule sync --recursive
2025-03-14T04:04:55.1046639Z Synchronizing submodule url for 'android/libs/fbjni'
2025-03-14T04:04:55.1066549Z Synchronizing submodule url for 'third_party/FP16'
2025-03-14T04:04:55.1086966Z Synchronizing submodule url for 'third_party/FXdiv'
2025-03-14T04:04:55.1107048Z Synchronizing submodule url for 'third_party/NNPACK'
2025-03-14T04:04:55.1128040Z Synchronizing submodule url for 'third_party/NVTX'
2025-03-14T04:04:55.1148975Z Synchronizing submodule url for 'third_party/VulkanMemoryAllocator'
2025-03-14T04:04:55.1169389Z Synchronizing submodule url for 'third_party/XNNPACK'
2025-03-14T04:04:55.1205217Z Synchronizing submodule url for 'third_party/benchmark'
2025-03-14T04:04:55.1225809Z Synchronizing submodule url for 'third_party/composable_kernel'
2025-03-14T04:04:55.1253658Z Synchronizing submodule url for 'third_party/cpp-httplib'
2025-03-14T04:04:55.1273498Z Synchronizing submodule url for 'third_party/cpuinfo'
2025-03-14T04:04:55.1294415Z Synchronizing submodule url for 'third_party/cudnn_frontend'
2025-03-14T04:04:55.1314604Z Synchronizing submodule url for 'third_party/cutlass'
2025-03-14T04:04:55.1343295Z Synchronizing submodule url for 'third_party/eigen'
2025-03-14T04:04:55.1366062Z Synchronizing submodule url for 'third_party/fbgemm'
2025-03-14T04:04:55.1384809Z Synchronizing submodule url for 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:04:55.1403388Z Synchronizing submodule url for 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:04:55.1422564Z Synchronizing submodule url for 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:04:55.1446640Z Synchronizing submodule url for 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:04:55.1465596Z Synchronizing submodule url for 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:04:55.1487749Z Synchronizing submodule url for 'third_party/flash-attention'
2025-03-14T04:04:55.1507658Z Synchronizing submodule url for 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:04:55.1531455Z Synchronizing submodule url for 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:04:55.1563265Z Synchronizing submodule url for 'third_party/flatbuffers'
2025-03-14T04:04:55.1586080Z Synchronizing submodule url for 'third_party/fmt'
2025-03-14T04:04:55.1606771Z Synchronizing submodule url for 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:04:55.1626991Z Synchronizing submodule url for 'third_party/gloo'
2025-03-14T04:04:55.1646884Z Synchronizing submodule url for 'third_party/googletest'
2025-03-14T04:04:55.1667937Z Synchronizing submodule url for 'third_party/ideep'
2025-03-14T04:04:55.1685211Z Synchronizing submodule url for 'third_party/ideep/mkl-dnn'
2025-03-14T04:04:55.1713355Z Synchronizing submodule url for 'third_party/ittapi'
2025-03-14T04:04:55.1734295Z Synchronizing submodule url for 'third_party/kineto'
2025-03-14T04:04:55.1754167Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:04:55.1773004Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:04:55.1793219Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:04:55.1812372Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:04:55.1832756Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:04:55.1850876Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:04:55.1870948Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:04:55.1890666Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:04:55.1910865Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:04:55.1932326Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:04:55.1952681Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:04:55.1974403Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:04:55.2008317Z Synchronizing submodule url for 'third_party/kleidiai'
2025-03-14T04:04:55.2019497Z Synchronizing submodule url for 'third_party/mimalloc'
2025-03-14T04:04:55.2040478Z Synchronizing submodule url for 'third_party/nlohmann'
2025-03-14T04:04:55.2064271Z Synchronizing submodule url for 'third_party/onnx'
2025-03-14T04:04:55.2099473Z Synchronizing submodule url for 'third_party/onnx/third_party/pybind11'
2025-03-14T04:04:55.2122406Z Synchronizing submodule url for 'third_party/opentelemetry-cpp'
2025-03-14T04:04:55.2143027Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:04:55.2162841Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:04:55.2181297Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:04:55.2199686Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:04:55.2219914Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:04:55.2237961Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:04:55.2257984Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:04:55.2275418Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:04:55.2296317Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:04:55.2317275Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:04:55.2358524Z Synchronizing submodule url for 'third_party/pocketfft'
2025-03-14T04:04:55.2379260Z Synchronizing submodule url for 'third_party/protobuf'
2025-03-14T04:04:55.2401111Z Synchronizing submodule url for 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:04:55.2419242Z Synchronizing submodule url for 'third_party/protobuf/third_party/googletest'
2025-03-14T04:04:55.2441078Z Synchronizing submodule url for 'third_party/psimd'
2025-03-14T04:04:55.2461551Z Synchronizing submodule url for 'third_party/pthreadpool'
2025-03-14T04:04:55.2481535Z Synchronizing submodule url for 'third_party/pybind11'
2025-03-14T04:04:55.2501837Z Synchronizing submodule url for 'third_party/python-peachpy'
2025-03-14T04:04:55.2522432Z Synchronizing submodule url for 'third_party/sleef'
2025-03-14T04:04:55.2543664Z Synchronizing submodule url for 'third_party/tensorpipe'
2025-03-14T04:04:55.2563747Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:04:55.2581541Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:04:55.2600139Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:04:55.2618920Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:04:55.2635855Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:04:55.2668358Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2025-03-14T04:04:55.3118378Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f'
2025-03-14T04:04:55.3241227Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3'
2025-03-14T04:04:55.3337729Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1'
2025-03-14T04:04:55.3613077Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73'
2025-03-14T04:04:55.4085693Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1'
2025-03-14T04:04:55.4548961Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191'
2025-03-14T04:04:56.3123285Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883'
2025-03-14T04:04:56.3378300Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415'
2025-03-14T04:04:56.6390149Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3'
2025-03-14T04:04:56.6894079Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006'
2025-03-14T04:04:56.8047063Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99'
2025-03-14T04:04:56.8450690Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a'
2025-03-14T04:04:57.5808130Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008'
2025-03-14T04:04:57.8592544Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c'
2025-03-14T04:04:57.9323665Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e'
2025-03-14T04:04:57.9816391Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81'
2025-03-14T04:04:58.0909101Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3'
2025-03-14T04:04:58.6033270Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6'
2025-03-14T04:04:58.6524437Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796'
2025-03-14T04:04:58.6650655Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1'
2025-03-14T04:04:58.7661609Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5'
2025-03-14T04:04:59.0591103Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33'
2025-03-14T04:04:59.7719716Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420'
2025-03-14T04:04:59.9296878Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b'
2025-03-14T04:04:59.9643622Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a'
2025-03-14T04:05:00.0071985Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350'
2025-03-14T04:05:00.0358104Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509'
2025-03-14T04:05:00.0848251Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59'
2025-03-14T04:05:00.0984202Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3'
2025-03-14T04:05:00.7301132Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d'
2025-03-14T04:05:00.7470290Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42'
2025-03-14T04:05:00.8563338Z Submodule path 'third_party/kineto': checked out '2859721fd9e73d3ca1c56f827dbc64e6d68f78a2'
2025-03-14T04:05:00.9496048Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e'
2025-03-14T04:05:01.1504032Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9'
2025-03-14T04:05:01.1707258Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400'
2025-03-14T04:05:01.2117445Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05'
2025-03-14T04:05:01.2260501Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067'
2025-03-14T04:05:01.2350070Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4'
2025-03-14T04:05:01.2545278Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446'
2025-03-14T04:05:01.2987795Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850'
2025-03-14T04:05:01.4300204Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5'
2025-03-14T04:05:01.4478049Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150'
2025-03-14T04:05:01.4883820Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164'
2025-03-14T04:05:01.5375434Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347'
2025-03-14T04:05:01.5844300Z Submodule path 'third_party/kleidiai': checked out 'ef685a13cfbe8d418aa2ed34350e21e4938358b6'
2025-03-14T04:05:01.6269976Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78'
2025-03-14T04:05:01.7682014Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806'
2025-03-14T04:05:02.2030586Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c'
2025-03-14T04:05:02.2444272Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b'
2025-03-14T04:05:02.3306595Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878'
2025-03-14T04:05:02.3533206Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2'
2025-03-14T04:05:02.3961366Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1'
2025-03-14T04:05:02.4093882Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa'
2025-03-14T04:05:02.5598159Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d'
2025-03-14T04:05:02.5730310Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce'
2025-03-14T04:05:02.5898592Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5'
2025-03-14T04:05:02.6079954Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d'
2025-03-14T04:05:02.8951641Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4'
2025-03-14T04:05:02.9453233Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929'
2025-03-14T04:05:03.5766160Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50'
2025-03-14T04:05:03.5883681Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe'
2025-03-14T04:05:03.8964575Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a'
2025-03-14T04:05:03.9132614Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8'
2025-03-14T04:05:03.9673873Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081'
2025-03-14T04:05:03.9773880Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900'
2025-03-14T04:05:03.9898849Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8'
2025-03-14T04:05:04.0324764Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4'
2025-03-14T04:05:04.0644124Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67'
2025-03-14T04:05:04.1181022Z Submodule path 'third_party/sleef': checked out '56e1f79cb140fb9326d612d0be06b5250565cade'
2025-03-14T04:05:04.1506694Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e'
2025-03-14T04:05:04.1991453Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e'
2025-03-14T04:05:04.2165958Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281'
2025-03-14T04:05:04.2879544Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242'
2025-03-14T04:05:04.3211333Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef'
2025-03-14T04:05:04.3302488Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5'
2025-03-14T04:05:04.3337600Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0
2025-03-14T04:05:04.3614511Z Entering 'android/libs/fbjni'
2025-03-14T04:05:04.3654552Z Entering 'third_party/FP16'
2025-03-14T04:05:04.3692225Z Entering 'third_party/FXdiv'
2025-03-14T04:05:04.3731504Z Entering 'third_party/NNPACK'
2025-03-14T04:05:04.3771838Z Entering 'third_party/NVTX'
2025-03-14T04:05:04.3809885Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:05:04.3848170Z Entering 'third_party/XNNPACK'
2025-03-14T04:05:04.3902547Z Entering 'third_party/benchmark'
2025-03-14T04:05:04.3940682Z Entering 'third_party/composable_kernel'
2025-03-14T04:05:04.3985061Z Entering 'third_party/cpp-httplib'
2025-03-14T04:05:04.4022392Z Entering 'third_party/cpuinfo'
2025-03-14T04:05:04.4061272Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:05:04.4099146Z Entering 'third_party/cutlass'
2025-03-14T04:05:04.4146459Z Entering 'third_party/eigen'
2025-03-14T04:05:04.4186913Z Entering 'third_party/fbgemm'
2025-03-14T04:05:04.4225748Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:05:04.4263334Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:05:04.4300255Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:05:04.4345267Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:05:04.4381532Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:05:04.4418973Z Entering 'third_party/flash-attention'
2025-03-14T04:05:04.4459597Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:05:04.4502396Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:05:04.4550414Z Entering 'third_party/flatbuffers'
2025-03-14T04:05:04.4590629Z Entering 'third_party/fmt'
2025-03-14T04:05:04.4628656Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:05:04.4667321Z Entering 'third_party/gloo'
2025-03-14T04:05:04.4704940Z Entering 'third_party/googletest'
2025-03-14T04:05:04.4744682Z Entering 'third_party/ideep'
2025-03-14T04:05:04.4781834Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:05:04.4827419Z Entering 'third_party/ittapi'
2025-03-14T04:05:04.4866081Z Entering 'third_party/kineto'
2025-03-14T04:05:04.4904340Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:05:04.4942704Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:05:04.4982048Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:05:04.5019103Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:05:04.5057495Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:05:04.5094125Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:05:04.5133179Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:05:04.5171720Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:05:04.5209069Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:05:04.5248835Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:05:04.5287726Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:05:04.5324603Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:05:04.5362891Z Entering 'third_party/kleidiai'
2025-03-14T04:05:04.5401825Z Entering 'third_party/mimalloc'
2025-03-14T04:05:04.5440582Z Entering 'third_party/nlohmann'
2025-03-14T04:05:04.5479972Z Entering 'third_party/onnx'
2025-03-14T04:05:04.5535774Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:05:04.5576681Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:05:04.5616843Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:05:04.5653918Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:05:04.5691058Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:05:04.5727531Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:05:04.5766894Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:05:04.5803291Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:05:04.5840681Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:05:04.5876669Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:05:04.5915367Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:05:04.5953760Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:05:04.6012205Z Entering 'third_party/pocketfft'
2025-03-14T04:05:04.6053317Z Entering 'third_party/protobuf'
2025-03-14T04:05:04.6094476Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:05:04.6132508Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:05:04.6171253Z Entering 'third_party/psimd'
2025-03-14T04:05:04.6208986Z Entering 'third_party/pthreadpool'
2025-03-14T04:05:04.6247382Z Entering 'third_party/pybind11'
2025-03-14T04:05:04.6286129Z Entering 'third_party/python-peachpy'
2025-03-14T04:05:04.6323672Z Entering 'third_party/sleef'
2025-03-14T04:05:04.6362238Z Entering 'third_party/tensorpipe'
2025-03-14T04:05:04.6400126Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:05:04.6437073Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:05:04.6473568Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:05:04.6510483Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:05:04.6547019Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:05:04.6596526Z ##[endgroup]
2025-03-14T04:05:04.6597032Z ##[group]Persisting credentials for submodules
2025-03-14T04:05:04.6603119Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :"
2025-03-14T04:05:04.6875440Z Entering 'android/libs/fbjni'
2025-03-14T04:05:04.6908602Z url.https://github.com/.insteadof
2025-03-14T04:05:04.6908981Z url.https://github.com/.insteadof
2025-03-14T04:05:04.6938715Z Entering 'third_party/FP16'
2025-03-14T04:05:04.6973196Z url.https://github.com/.insteadof
2025-03-14T04:05:04.6973590Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7003048Z Entering 'third_party/FXdiv'
2025-03-14T04:05:04.7036781Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7037215Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7066204Z Entering 'third_party/NNPACK'
2025-03-14T04:05:04.7099585Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7099964Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7129915Z Entering 'third_party/NVTX'
2025-03-14T04:05:04.7164585Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7164977Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7194346Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:05:04.7229431Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7229840Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7260514Z Entering 'third_party/XNNPACK'
2025-03-14T04:05:04.7293536Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7293922Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7339522Z Entering 'third_party/benchmark'
2025-03-14T04:05:04.7373167Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7373568Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7402655Z Entering 'third_party/composable_kernel'
2025-03-14T04:05:04.7436255Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7436610Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7472760Z Entering 'third_party/cpp-httplib'
2025-03-14T04:05:04.7505977Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7506358Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7536131Z Entering 'third_party/cpuinfo'
2025-03-14T04:05:04.7570184Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7570602Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7600160Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:05:04.7634292Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7634652Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7666794Z Entering 'third_party/cutlass'
2025-03-14T04:05:04.7700390Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7700772Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7739626Z Entering 'third_party/eigen'
2025-03-14T04:05:04.7773479Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7773861Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7805872Z Entering 'third_party/fbgemm'
2025-03-14T04:05:04.7839715Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7840134Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7870301Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:05:04.7902552Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7902935Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7932110Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:05:04.7964788Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7965169Z url.https://github.com/.insteadof
2025-03-14T04:05:04.7994363Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:05:04.8027634Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8028016Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8063964Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:05:04.8096170Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8096550Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8126279Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:05:04.8159271Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8159633Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8190308Z Entering 'third_party/flash-attention'
2025-03-14T04:05:04.8223919Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8224294Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8256027Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:05:04.8288748Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8289129Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8325248Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:05:04.8360112Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8360472Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8400063Z Entering 'third_party/flatbuffers'
2025-03-14T04:05:04.8433351Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8433723Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8465688Z Entering 'third_party/fmt'
2025-03-14T04:05:04.8499011Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8499383Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8528619Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:05:04.8563648Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8564006Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8593451Z Entering 'third_party/gloo'
2025-03-14T04:05:04.8628017Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8628402Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8658253Z Entering 'third_party/googletest'
2025-03-14T04:05:04.8691806Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8692174Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8722283Z Entering 'third_party/ideep'
2025-03-14T04:05:04.8755772Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8756253Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8785085Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:05:04.8818357Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8818722Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8856615Z Entering 'third_party/ittapi'
2025-03-14T04:05:04.8889987Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8890346Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8919837Z Entering 'third_party/kineto'
2025-03-14T04:05:04.8952710Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8953082Z url.https://github.com/.insteadof
2025-03-14T04:05:04.8982473Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:05:04.9014975Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9015349Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9045230Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:05:04.9077964Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9078318Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9109450Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:05:04.9142019Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9142391Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9173677Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:05:04.9206315Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9206691Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9236547Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:05:04.9269638Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9269993Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9299282Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:05:04.9334334Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9334837Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9365521Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:05:04.9397538Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9398209Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9428926Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:05:04.9461874Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9462522Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9491822Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:05:04.9525594Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9526433Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9557677Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:05:04.9589798Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9590446Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9621070Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:05:04.9655731Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9656174Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9685675Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:05:04.9718453Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9718817Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9751599Z Entering 'third_party/kleidiai'
2025-03-14T04:05:04.9786242Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9786630Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9816552Z Entering 'third_party/mimalloc'
2025-03-14T04:05:04.9851360Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9851742Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9881951Z Entering 'third_party/nlohmann'
2025-03-14T04:05:04.9915475Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9915835Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9946944Z Entering 'third_party/onnx'
2025-03-14T04:05:04.9981146Z url.https://github.com/.insteadof
2025-03-14T04:05:04.9981512Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0027255Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:05:05.0060664Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0061040Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0092044Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:05:05.0127391Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0127936Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0159216Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:05:05.0191357Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0192038Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0220735Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:05:05.0254677Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0255315Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0284930Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:05:05.0318044Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0318678Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0347721Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:05:05.0380648Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0381294Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0412074Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:05:05.0446227Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0446641Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0475838Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:05:05.0508271Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0508654Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0538334Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:05:05.0571868Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0572254Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0601434Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:05:05.0634913Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0635301Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0667381Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:05:05.0700349Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0700738Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0732333Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:05:05.0765412Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0765782Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0816462Z Entering 'third_party/pocketfft'
2025-03-14T04:05:05.0850923Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0851301Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0880840Z Entering 'third_party/protobuf'
2025-03-14T04:05:05.0915559Z url.https://github.com/.insteadof
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2025-03-14T04:05:05.0950090Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:05:05.0983275Z url.https://github.com/.insteadof
2025-03-14T04:05:05.0983649Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1061951Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:05:05.1095129Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1095519Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1127360Z Entering 'third_party/psimd'
2025-03-14T04:05:05.1161963Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1162338Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1192632Z Entering 'third_party/pthreadpool'
2025-03-14T04:05:05.1226251Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1226632Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1258155Z Entering 'third_party/pybind11'
2025-03-14T04:05:05.1291536Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1291912Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1322590Z Entering 'third_party/python-peachpy'
2025-03-14T04:05:05.1356500Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1356887Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1388697Z Entering 'third_party/sleef'
2025-03-14T04:05:05.1423428Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1423856Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1455311Z Entering 'third_party/tensorpipe'
2025-03-14T04:05:05.1488867Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1489251Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1519922Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:05:05.1553372Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1553769Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1583391Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:05:05.1616778Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1617176Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1648560Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:05:05.1681495Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1682046Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1711323Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:05:05.1744671Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1745105Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1773263Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:05:05.1805922Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1806310Z url.https://github.com/.insteadof
2025-03-14T04:05:05.1853463Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url"
2025-03-14T04:05:05.2129075Z Entering 'android/libs/fbjni'
2025-03-14T04:05:05.2174884Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2025-03-14T04:05:05.2190388Z Entering 'third_party/FP16'
2025-03-14T04:05:05.2236485Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2025-03-14T04:05:05.2251524Z Entering 'third_party/FXdiv'
2025-03-14T04:05:05.2298118Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2025-03-14T04:05:05.2312777Z Entering 'third_party/NNPACK'
2025-03-14T04:05:05.2359905Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2025-03-14T04:05:05.2375481Z Entering 'third_party/NVTX'
2025-03-14T04:05:05.2422085Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config	remote.origin.url
2025-03-14T04:05:05.2437837Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:05:05.2483532Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2025-03-14T04:05:05.2498480Z Entering 'third_party/XNNPACK'
2025-03-14T04:05:05.2544336Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
2025-03-14T04:05:05.2575167Z Entering 'third_party/benchmark'
2025-03-14T04:05:05.2621062Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:05:05.2636221Z Entering 'third_party/composable_kernel'
2025-03-14T04:05:05.2683180Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config	remote.origin.url
2025-03-14T04:05:05.2705941Z Entering 'third_party/cpp-httplib'
2025-03-14T04:05:05.2753092Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config	remote.origin.url
2025-03-14T04:05:05.2768147Z Entering 'third_party/cpuinfo'
2025-03-14T04:05:05.2814219Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
2025-03-14T04:05:05.2829569Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:05:05.2875073Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
2025-03-14T04:05:05.2890359Z Entering 'third_party/cutlass'
2025-03-14T04:05:05.2936433Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
2025-03-14T04:05:05.2962233Z Entering 'third_party/eigen'
2025-03-14T04:05:05.3008011Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
2025-03-14T04:05:05.3025388Z Entering 'third_party/fbgemm'
2025-03-14T04:05:05.3071489Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2025-03-14T04:05:05.3086614Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:05:05.3131747Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
2025-03-14T04:05:05.3146145Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:05:05.3194447Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
2025-03-14T04:05:05.3209026Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:05:05.3257182Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cutlass/config	remote.origin.url
2025-03-14T04:05:05.3278845Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:05:05.3324965Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:05:05.3339720Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:05:05.3387073Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
2025-03-14T04:05:05.3402562Z Entering 'third_party/flash-attention'
2025-03-14T04:05:05.3449934Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/config	remote.origin.url
2025-03-14T04:05:05.3465053Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:05:05.3510837Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/composable_kernel/config	remote.origin.url
2025-03-14T04:05:05.3531654Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:05:05.3577399Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config	remote.origin.url
2025-03-14T04:05:05.3601751Z Entering 'third_party/flatbuffers'
2025-03-14T04:05:05.3648232Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2025-03-14T04:05:05.3665804Z Entering 'third_party/fmt'
2025-03-14T04:05:05.3711774Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
2025-03-14T04:05:05.3727084Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:05:05.3773472Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2025-03-14T04:05:05.3788147Z Entering 'third_party/gloo'
2025-03-14T04:05:05.3836035Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
2025-03-14T04:05:05.3851616Z Entering 'third_party/googletest'
2025-03-14T04:05:05.3897467Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:05:05.3912255Z Entering 'third_party/ideep'
2025-03-14T04:05:05.3961145Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2025-03-14T04:05:05.3974441Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:05:05.4019875Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2025-03-14T04:05:05.4043567Z Entering 'third_party/ittapi'
2025-03-14T04:05:05.4088793Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
2025-03-14T04:05:05.4103773Z Entering 'third_party/kineto'
2025-03-14T04:05:05.4152713Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2025-03-14T04:05:05.4167631Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:05:05.4214633Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config	remote.origin.url
2025-03-14T04:05:05.4228499Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:05:05.4276470Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config	remote.origin.url
2025-03-14T04:05:05.4292056Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:05:05.4340272Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config	remote.origin.url
2025-03-14T04:05:05.4354915Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:05:05.4402204Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config	remote.origin.url
2025-03-14T04:05:05.4416768Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:05:05.4467156Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config	remote.origin.url
2025-03-14T04:05:05.4480121Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:05:05.4528494Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config	remote.origin.url
2025-03-14T04:05:05.4544356Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:05:05.4591743Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config	remote.origin.url
2025-03-14T04:05:05.4606015Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:05:05.4653613Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:05:05.4667504Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:05:05.4717310Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config	remote.origin.url
2025-03-14T04:05:05.4733607Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:05:05.4782588Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config	remote.origin.url
2025-03-14T04:05:05.4798346Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:05:05.4847223Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config	remote.origin.url
2025-03-14T04:05:05.4862133Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:05:05.4908050Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config	remote.origin.url
2025-03-14T04:05:05.4924520Z Entering 'third_party/kleidiai'
2025-03-14T04:05:05.4972325Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kleidiai/config	remote.origin.url
2025-03-14T04:05:05.4988030Z Entering 'third_party/mimalloc'
2025-03-14T04:05:05.5035228Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config	remote.origin.url
2025-03-14T04:05:05.5051327Z Entering 'third_party/nlohmann'
2025-03-14T04:05:05.5099060Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config	remote.origin.url
2025-03-14T04:05:05.5115843Z Entering 'third_party/onnx'
2025-03-14T04:05:05.5164766Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config	remote.origin.url
2025-03-14T04:05:05.5195954Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:05:05.5242922Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:05:05.5260478Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:05:05.5309039Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config	remote.origin.url
2025-03-14T04:05:05.5325672Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:05:05.5372409Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:05:05.5386653Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:05:05.5433317Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:05:05.5447531Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:05:05.5495118Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config	remote.origin.url
2025-03-14T04:05:05.5508891Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:05:05.5556278Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config	remote.origin.url
2025-03-14T04:05:05.5572244Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:05:05.5618202Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config	remote.origin.url
2025-03-14T04:05:05.5631984Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:05:05.5678579Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config	remote.origin.url
2025-03-14T04:05:05.5692589Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:05:05.5739879Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config	remote.origin.url
2025-03-14T04:05:05.5753279Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:05:05.5801596Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config	remote.origin.url
2025-03-14T04:05:05.5818190Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:05:05.5867538Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config	remote.origin.url
2025-03-14T04:05:05.5884232Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:05:05.5930662Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config	remote.origin.url
2025-03-14T04:05:05.5965786Z Entering 'third_party/pocketfft'
2025-03-14T04:05:05.6012586Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config	remote.origin.url
2025-03-14T04:05:05.6028069Z Entering 'third_party/protobuf'
2025-03-14T04:05:05.6076013Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config	remote.origin.url
2025-03-14T04:05:05.6094433Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:05:05.6140761Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:05:05.6157192Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:05:05.6203371Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:05:05.6220051Z Entering 'third_party/psimd'
2025-03-14T04:05:05.6266727Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config	remote.origin.url
2025-03-14T04:05:05.6281818Z Entering 'third_party/pthreadpool'
2025-03-14T04:05:05.6329613Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config	remote.origin.url
2025-03-14T04:05:05.6345385Z Entering 'third_party/pybind11'
2025-03-14T04:05:05.6392752Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:05:05.6408808Z Entering 'third_party/python-peachpy'
2025-03-14T04:05:05.6458859Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config	remote.origin.url
2025-03-14T04:05:05.6473996Z Entering 'third_party/sleef'
2025-03-14T04:05:05.6520689Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config	remote.origin.url
2025-03-14T04:05:05.6536149Z Entering 'third_party/tensorpipe'
2025-03-14T04:05:05.6584475Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config	remote.origin.url
2025-03-14T04:05:05.6599295Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:05:05.6645603Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:05:05.6660364Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:05:05.6706844Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config	remote.origin.url
2025-03-14T04:05:05.6721408Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:05:05.6769294Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config	remote.origin.url
2025-03-14T04:05:05.6783593Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:05:05.6830255Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:05:05.6844996Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:05:05.6891733Z 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
2025-03-14T04:05:05.7789045Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
2025-03-14T04:05:05.8069644Z Entering 'android/libs/fbjni'
2025-03-14T04:05:05.8107359Z Entering 'third_party/FP16'
2025-03-14T04:05:05.8146967Z Entering 'third_party/FXdiv'
2025-03-14T04:05:05.8185710Z Entering 'third_party/NNPACK'
2025-03-14T04:05:05.8224486Z Entering 'third_party/NVTX'
2025-03-14T04:05:05.8263178Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:05:05.8301701Z Entering 'third_party/XNNPACK'
2025-03-14T04:05:05.8356978Z Entering 'third_party/benchmark'
2025-03-14T04:05:05.8395381Z Entering 'third_party/composable_kernel'
2025-03-14T04:05:05.8441204Z Entering 'third_party/cpp-httplib'
2025-03-14T04:05:05.8479075Z Entering 'third_party/cpuinfo'
2025-03-14T04:05:05.8517273Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:05:05.8556629Z Entering 'third_party/cutlass'
2025-03-14T04:05:05.8604370Z Entering 'third_party/eigen'
2025-03-14T04:05:05.8644194Z Entering 'third_party/fbgemm'
2025-03-14T04:05:05.8682277Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:05:05.8718547Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:05:05.8755146Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:05:05.8798364Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:05:05.8834291Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:05:05.8871844Z Entering 'third_party/flash-attention'
2025-03-14T04:05:05.8910611Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:05:05.8955626Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:05:05.9001783Z Entering 'third_party/flatbuffers'
2025-03-14T04:05:05.9042634Z Entering 'third_party/fmt'
2025-03-14T04:05:05.9082004Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:05:05.9119818Z Entering 'third_party/gloo'
2025-03-14T04:05:05.9158419Z Entering 'third_party/googletest'
2025-03-14T04:05:05.9195968Z Entering 'third_party/ideep'
2025-03-14T04:05:05.9232999Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:05:05.9278792Z Entering 'third_party/ittapi'
2025-03-14T04:05:05.9315872Z Entering 'third_party/kineto'
2025-03-14T04:05:05.9354589Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:05:05.9393305Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:05:05.9431613Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:05:05.9470530Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:05:05.9507660Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:05:05.9544926Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:05:05.9583620Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:05:05.9620639Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:05:05.9657887Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:05:05.9695996Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:05:05.9734455Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:05:05.9772354Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:05:05.9810847Z Entering 'third_party/kleidiai'
2025-03-14T04:05:05.9849654Z Entering 'third_party/mimalloc'
2025-03-14T04:05:05.9889023Z Entering 'third_party/nlohmann'
2025-03-14T04:05:05.9928238Z Entering 'third_party/onnx'
2025-03-14T04:05:05.9987290Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:05:06.0026881Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:05:06.0068407Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:05:06.0104823Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:05:06.0142047Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:05:06.0178521Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:05:06.0216515Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:05:06.0252973Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:05:06.0289230Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:05:06.0325844Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:05:06.0366098Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:05:06.0404303Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:05:06.0462892Z Entering 'third_party/pocketfft'
2025-03-14T04:05:06.0502746Z Entering 'third_party/protobuf'
2025-03-14T04:05:06.0544268Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:05:06.0580926Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:05:06.0620317Z Entering 'third_party/psimd'
2025-03-14T04:05:06.0658626Z Entering 'third_party/pthreadpool'
2025-03-14T04:05:06.0697147Z Entering 'third_party/pybind11'
2025-03-14T04:05:06.0735532Z Entering 'third_party/python-peachpy'
2025-03-14T04:05:06.0773866Z Entering 'third_party/sleef'
2025-03-14T04:05:06.0812669Z Entering 'third_party/tensorpipe'
2025-03-14T04:05:06.0851444Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:05:06.0887707Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:05:06.0923623Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:05:06.0961870Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:05:06.0997613Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:05:06.1049416Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:'
2025-03-14T04:05:06.1322702Z Entering 'android/libs/fbjni'
2025-03-14T04:05:06.1360825Z Entering 'third_party/FP16'
2025-03-14T04:05:06.1399182Z Entering 'third_party/FXdiv'
2025-03-14T04:05:06.1438978Z Entering 'third_party/NNPACK'
2025-03-14T04:05:06.1476857Z Entering 'third_party/NVTX'
2025-03-14T04:05:06.1515112Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:05:06.1553920Z Entering 'third_party/XNNPACK'
2025-03-14T04:05:06.1607262Z Entering 'third_party/benchmark'
2025-03-14T04:05:06.1645125Z Entering 'third_party/composable_kernel'
2025-03-14T04:05:06.1689373Z Entering 'third_party/cpp-httplib'
2025-03-14T04:05:06.1727398Z Entering 'third_party/cpuinfo'
2025-03-14T04:05:06.1765993Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:05:06.1804775Z Entering 'third_party/cutlass'
2025-03-14T04:05:06.1850369Z Entering 'third_party/eigen'
2025-03-14T04:05:06.1890822Z Entering 'third_party/fbgemm'
2025-03-14T04:05:06.1930811Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:05:06.1967428Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:05:06.2004156Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:05:06.2047880Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:05:06.2084443Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:05:06.2122036Z Entering 'third_party/flash-attention'
2025-03-14T04:05:06.2161349Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:05:06.2205066Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:05:06.2252828Z Entering 'third_party/flatbuffers'
2025-03-14T04:05:06.2293309Z Entering 'third_party/fmt'
2025-03-14T04:05:06.2331833Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:05:06.2369724Z Entering 'third_party/gloo'
2025-03-14T04:05:06.2408002Z Entering 'third_party/googletest'
2025-03-14T04:05:06.2446545Z Entering 'third_party/ideep'
2025-03-14T04:05:06.2483416Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:05:06.2527844Z Entering 'third_party/ittapi'
2025-03-14T04:05:06.2565983Z Entering 'third_party/kineto'
2025-03-14T04:05:06.2603946Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:05:06.2641782Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:05:06.2682854Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:05:06.2720702Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:05:06.2758386Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:05:06.2794910Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:05:06.2834413Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:05:06.2871335Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:05:06.2908728Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:05:06.2946363Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:05:06.2984914Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:05:06.3021823Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:05:06.3060541Z Entering 'third_party/kleidiai'
2025-03-14T04:05:06.3099406Z Entering 'third_party/mimalloc'
2025-03-14T04:05:06.3137417Z Entering 'third_party/nlohmann'
2025-03-14T04:05:06.3180107Z Entering 'third_party/onnx'
2025-03-14T04:05:06.3235130Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:05:06.3276089Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:05:06.3315933Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:05:06.3354333Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:05:06.3391241Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:05:06.3428596Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:05:06.3467848Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:05:06.3505853Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:05:06.3542180Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:05:06.3580339Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:05:06.3620027Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:05:06.3659060Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:05:06.3717060Z Entering 'third_party/pocketfft'
2025-03-14T04:05:06.3756450Z Entering 'third_party/protobuf'
2025-03-14T04:05:06.3797802Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:05:06.3834465Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:05:06.3874128Z Entering 'third_party/psimd'
2025-03-14T04:05:06.3911584Z Entering 'third_party/pthreadpool'
2025-03-14T04:05:06.3950227Z Entering 'third_party/pybind11'
2025-03-14T04:05:06.3987778Z Entering 'third_party/python-peachpy'
2025-03-14T04:05:06.4025565Z Entering 'third_party/sleef'
2025-03-14T04:05:06.4064603Z Entering 'third_party/tensorpipe'
2025-03-14T04:05:06.4102058Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:05:06.4138639Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:05:06.4178125Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:05:06.4214703Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:05:06.4252003Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:05:06.4300804Z ##[endgroup]
2025-03-14T04:05:06.4338077Z [command]/usr/bin/git log -1 --format=%H
2025-03-14T04:05:06.4358889Z aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:05:06.4526318Z Prepare all required actions
2025-03-14T04:05:06.4526935Z Getting action download info
2025-03-14T04:05:06.5604780Z ##[group]Run ./.github/actions/setup-linux
2025-03-14T04:05:06.5605144Z env:
2025-03-14T04:05:06.5605391Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:06.5605682Z ##[endgroup]
2025-03-14T04:05:06.5655408Z ##[group]Run set -euo pipefail
2025-03-14T04:05:06.5655805Z set -euo pipefail
2025-03-14T04:05:06.5656127Z function get_ec2_metadata() {
2025-03-14T04:05:06.5656557Z   # Pulled from instance metadata endpoint for EC2
2025-03-14T04:05:06.5657251Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2025-03-14T04:05:06.5657840Z   category=$1
2025-03-14T04:05:06.5658230Z   # If it is GCP runner (runner name contains gcp), do not run this
2025-03-14T04:05:06.5658695Z   runner_name_str=i-01e2d8c57795e0761
2025-03-14T04:05:06.5659107Z   if [[ -f /.inarc ]]; then
2025-03-14T04:05:06.5659479Z     echo "ARC Runner, no info on ec2 metadata"
2025-03-14T04:05:06.5659894Z   elif [[ $runner_name_str == *"gcp"* ]]; then
2025-03-14T04:05:06.5660398Z     echo "Runner is from Google Cloud Platform, No info on ec2 metadata"
2025-03-14T04:05:06.5660853Z   else
2025-03-14T04:05:06.5661745Z     curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}"
2025-03-14T04:05:06.5662805Z   fi
2025-03-14T04:05:06.5663048Z }
2025-03-14T04:05:06.5663340Z echo "ami-id: $(get_ec2_metadata ami-id)"
2025-03-14T04:05:06.5663797Z echo "instance-id: $(get_ec2_metadata instance-id)"
2025-03-14T04:05:06.5664289Z echo "instance-type: $(get_ec2_metadata instance-type)"
2025-03-14T04:05:06.5664735Z echo "system info $(uname -a)"
2025-03-14T04:05:06.5671288Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:06.5671699Z env:
2025-03-14T04:05:06.5671942Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:06.5672218Z ##[endgroup]
2025-03-14T04:05:06.5804325Z ami-id: ami-05b10e08d247fb927
2025-03-14T04:05:06.5896416Z instance-id: i-01e2d8c57795e0761
2025-03-14T04:05:06.5989211Z instance-type: c5.4xlarge
2025-03-14T04:05:06.5999074Z system info Linux ip-10-0-66-204.ec2.internal 6.1.128-136.201.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Mon Feb 10 16:18:01 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux
2025-03-14T04:05:06.6029359Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:05:06.6030338Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:05:06.6036425Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:06.6036853Z env:
2025-03-14T04:05:06.6037112Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:06.6037399Z ##[endgroup]
2025-03-14T04:05:06.6094932Z ##[group]Run if systemctl is-active --quiet docker; then
2025-03-14T04:05:06.6095422Z if systemctl is-active --quiet docker; then
2025-03-14T04:05:06.6095900Z     echo "Docker daemon is running...";
2025-03-14T04:05:06.6096268Z else
2025-03-14T04:05:06.6096655Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2025-03-14T04:05:06.6097178Z fi
2025-03-14T04:05:06.6102462Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:06.6102868Z env:
2025-03-14T04:05:06.6103110Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:06.6103405Z ##[endgroup]
2025-03-14T04:05:06.6170460Z Docker daemon is running...
2025-03-14T04:05:06.6218089Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T04:05:06.6218416Z with:
2025-03-14T04:05:06.6218652Z   shell: bash
2025-03-14T04:05:06.6219075Z   timeout_minutes: 5
2025-03-14T04:05:06.6219355Z   max_attempts: 3
2025-03-14T04:05:06.6219629Z   retry_wait_seconds: 30
2025-03-14T04:05:06.6221991Z   command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
    --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"

# For LF Runners we need to make sure we also login to Meta's ECR docker registry too.
META_AWS_ACCOUNT_ID=308535385114
if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then
    aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
        --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
fi

2025-03-14T04:05:06.6224383Z   polling_interval_seconds: 1
2025-03-14T04:05:06.6224693Z   warning_on_retry: true
2025-03-14T04:05:06.6224982Z   continue_on_error: false
2025-03-14T04:05:06.6225269Z env:
2025-03-14T04:05:06.6225509Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:06.6225801Z   AWS_RETRY_MODE: standard
2025-03-14T04:05:06.6226072Z   AWS_MAX_ATTEMPTS: 5
2025-03-14T04:05:06.6226355Z   AWS_DEFAULT_REGION: us-east-1
2025-03-14T04:05:06.6226653Z ##[endgroup]
2025-03-14T04:05:07.7494025Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:05:07.7494716Z Configure a credential helper to remove this warning. See
2025-03-14T04:05:07.7495335Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:05:07.7495986Z 
2025-03-14T04:05:07.7496109Z Login Succeeded
2025-03-14T04:05:08.2698641Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:05:08.2699392Z Configure a credential helper to remove this warning. See
2025-03-14T04:05:08.2700043Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:05:08.2700447Z 
2025-03-14T04:05:08.2700574Z Login Succeeded
2025-03-14T04:05:08.7046074Z Command completed after 1 attempt(s).
2025-03-14T04:05:08.7118518Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:05:08.7119096Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:05:08.7119741Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:05:08.7125701Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:08.7126112Z env:
2025-03-14T04:05:08.7126362Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:08.7126667Z ##[endgroup]
2025-03-14T04:05:08.7220848Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T04:05:08.7221454Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T04:05:08.7221920Z # shellcheck disable=SC2046
2025-03-14T04:05:08.7222289Z docker stop $(docker ps -q) || true
2025-03-14T04:05:08.7222666Z # Prune all of the docker images
2025-03-14T04:05:08.7223046Z docker system prune -af
2025-03-14T04:05:08.7228529Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:08.7228940Z env:
2025-03-14T04:05:08.7229186Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:08.7229476Z ##[endgroup]
2025-03-14T04:05:08.7454940Z "docker stop" requires at least 1 argument.
2025-03-14T04:05:08.7455366Z See 'docker stop --help'.
2025-03-14T04:05:08.7455575Z 
2025-03-14T04:05:08.7455752Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2025-03-14T04:05:08.7456049Z 
2025-03-14T04:05:08.7456199Z Stop one or more running containers
2025-03-14T04:05:08.7599110Z Total reclaimed space: 0B
2025-03-14T04:05:08.7637814Z ##[group]Run set +e
2025-03-14T04:05:08.7638148Z set +e
2025-03-14T04:05:08.7638419Z set -x
2025-03-14T04:05:08.7638675Z 
2025-03-14T04:05:08.7638933Z PT_DOMAIN=download.pytorch.org
2025-03-14T04:05:08.7639630Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400,
2025-03-14T04:05:08.7640588Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last
2025-03-14T04:05:08.7641149Z # one is returned at random
2025-03-14T04:05:08.7641574Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1)
2025-03-14T04:05:08.7641989Z 
2025-03-14T04:05:08.7642247Z if [ -z "${RESOLVED_IP}" ]; then
2025-03-14T04:05:08.7642708Z   echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..."
2025-03-14T04:05:08.7643275Z   RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1)
2025-03-14T04:05:08.7643698Z 
2025-03-14T04:05:08.7643963Z   if [ -z "${RESOLVED_IP}" ]; then
2025-03-14T04:05:08.7644378Z     echo "Couldn't resolve ${PT_DOMAIN}, exiting..."
2025-03-14T04:05:08.7644769Z     exit 1
2025-03-14T04:05:08.7645033Z   fi
2025-03-14T04:05:08.7645272Z fi
2025-03-14T04:05:08.7645508Z 
2025-03-14T04:05:08.7645797Z if grep -r "${PT_DOMAIN}" /etc/hosts; then
2025-03-14T04:05:08.7646193Z   # Clean up any old records first
2025-03-14T04:05:08.7646589Z   sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts
2025-03-14T04:05:08.7646961Z fi
2025-03-14T04:05:08.7647197Z 
2025-03-14T04:05:08.7647523Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts
2025-03-14T04:05:08.7647952Z cat /etc/hosts
2025-03-14T04:05:08.7654094Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:08.7654625Z env:
2025-03-14T04:05:08.7654868Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:08.7655167Z ##[endgroup]
2025-03-14T04:05:08.7677370Z + PT_DOMAIN=download.pytorch.org
2025-03-14T04:05:08.7682473Z ++ dig -4 +short download.pytorch.org
2025-03-14T04:05:08.7683457Z ++ tail -n1
2025-03-14T04:05:08.8060197Z + RESOLVED_IP=18.160.10.22
2025-03-14T04:05:08.8060584Z + '[' -z 18.160.10.22 ']'
2025-03-14T04:05:08.8060935Z + grep -r download.pytorch.org /etc/hosts
2025-03-14T04:05:08.8071435Z 18.160.10.36 download.pytorch.org
2025-03-14T04:05:08.8072390Z + sudo sed -i /download.pytorch.org/d /etc/hosts
2025-03-14T04:05:09.1785756Z + echo '18.160.10.22 download.pytorch.org'
2025-03-14T04:05:09.1786524Z + sudo tee -a /etc/hosts
2025-03-14T04:05:09.3794662Z 18.160.10.22 download.pytorch.org
2025-03-14T04:05:09.3809206Z + cat /etc/hosts
2025-03-14T04:05:09.3817361Z 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
2025-03-14T04:05:09.3823673Z ::1         localhost6 localhost6.localdomain6
2025-03-14T04:05:09.3824089Z 18.160.10.22 download.pytorch.org
2025-03-14T04:05:09.3989574Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main
2025-03-14T04:05:09.3990082Z with:
2025-03-14T04:05:09.3990817Z   docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.3991628Z   docker-build-dir: .ci/docker
2025-03-14T04:05:09.3991962Z   working-directory: .
2025-03-14T04:05:09.3992361Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:09.3992805Z   force-push: false
2025-03-14T04:05:09.3993083Z env:
2025-03-14T04:05:09.3993340Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:09.3993647Z ##[endgroup]
2025-03-14T04:05:09.4022619Z ##[group]Run set -ex
2025-03-14T04:05:09.4022956Z set -ex
2025-03-14T04:05:09.4023194Z 
2025-03-14T04:05:09.4023616Z # If the docker build directory or the build script doesn't exist, the action will
2025-03-14T04:05:09.4024390Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2025-03-14T04:05:09.4024991Z # job could then download the pre-built image as usual
2025-03-14T04:05:09.4025536Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then
2025-03-14T04:05:09.4026042Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4026523Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4026959Z 
2025-03-14T04:05:09.4027336Z   echo "There is no Docker build script in ${REPO_NAME} repo, skipping..."
2025-03-14T04:05:09.4027807Z   exit 0
2025-03-14T04:05:09.4028059Z else
2025-03-14T04:05:09.4028342Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4028692Z fi
2025-03-14T04:05:09.4028928Z 
2025-03-14T04:05:09.4029300Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2025-03-14T04:05:09.4029932Z   # The docker image name already includes the ECR prefix and tag, so we can just
2025-03-14T04:05:09.4030495Z   # use it as it is, but first let's extract the tag
2025-03-14T04:05:09.4031002Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2025-03-14T04:05:09.4031547Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4032071Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4032513Z else
2025-03-14T04:05:09.4032856Z   DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2025-03-14T04:05:09.4033345Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4034014Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4034743Z fi
2025-03-14T04:05:09.4041831Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:09.4042241Z env:
2025-03-14T04:05:09.4042493Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:09.4042793Z   REPO_NAME: pytorch
2025-03-14T04:05:09.4043520Z   DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.4044305Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-14T04:05:09.4044710Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:09.4045132Z ##[endgroup]
2025-03-14T04:05:09.4068801Z + [[ ! -d .ci/docker ]]
2025-03-14T04:05:09.4069091Z + [[ ! -f .ci/docker/build.sh ]]
2025-03-14T04:05:09.4069425Z + echo skip=false
2025-03-14T04:05:09.4070489Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]]
2025-03-14T04:05:09.4076613Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.4077495Z ++ awk -F '[:,]' '{print $2}'
2025-03-14T04:05:09.4095573Z + DOCKER_TAG=aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.4096074Z + echo docker-tag=aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.4097075Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.4129756Z ##[group]Run set +e
2025-03-14T04:05:09.4130096Z set +e
2025-03-14T04:05:09.4130360Z set -x
2025-03-14T04:05:09.4130610Z 
2025-03-14T04:05:09.4130850Z login() {
2025-03-14T04:05:09.4131360Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-14T04:05:09.4131912Z }
2025-03-14T04:05:09.4132172Z 
2025-03-14T04:05:09.4132429Z retry () {
2025-03-14T04:05:09.4132739Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-14T04:05:09.4133086Z }
2025-03-14T04:05:09.4133312Z 
2025-03-14T04:05:09.4133583Z retry login "${DOCKER_REGISTRY}"
2025-03-14T04:05:09.4133927Z 
2025-03-14T04:05:09.4134179Z START_TIME=$(date +%s)
2025-03-14T04:05:09.4134509Z # Wait up to 120 minutes
2025-03-14T04:05:09.4134910Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do
2025-03-14T04:05:09.4135439Z   # Check if image already exists, if it does then skip building it
2025-03-14T04:05:09.4135969Z   if docker manifest inspect "${DOCKER_IMAGE}"; then
2025-03-14T04:05:09.4136370Z     exit 0
2025-03-14T04:05:09.4136641Z   fi
2025-03-14T04:05:09.4136896Z 
2025-03-14T04:05:09.4137318Z   # NB: This flag is used by Docker build workflow to push the image to ECR, so we can
2025-03-14T04:05:09.4138031Z   # use this to differentiate between the Docker build and regular build jobs. For the
2025-03-14T04:05:09.4138734Z   # latter, it will wait for the Docker images to become available before continuing
2025-03-14T04:05:09.4139288Z   if [ "${DOCKER_PUSH:-false}" == "true" ]; then
2025-03-14T04:05:09.4139717Z     # It's a Docker build job, let's build the image
2025-03-14T04:05:09.4140093Z     break
2025-03-14T04:05:09.4140350Z   else
2025-03-14T04:05:09.4140723Z     # It's a regular build job, wait for the image to become available
2025-03-14T04:05:09.4141164Z     sleep 300
2025-03-14T04:05:09.4141436Z   fi
2025-03-14T04:05:09.4141675Z done
2025-03-14T04:05:09.4141899Z 
2025-03-14T04:05:09.4142278Z # NB: This part requires a full checkout. Otherwise, the merge base will
2025-03-14T04:05:09.4142877Z # be empty.  The default action would be to continue rebuild the image
2025-03-14T04:05:09.4143558Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then
2025-03-14T04:05:09.4144040Z   # if we're on the base branch then use the parent commit
2025-03-14T04:05:09.4144471Z   MERGE_BASE=$(git rev-parse HEAD~)
2025-03-14T04:05:09.4144820Z else
2025-03-14T04:05:09.4145174Z   # otherwise we're on a PR, so use the most recent base commit
2025-03-14T04:05:09.4145683Z   MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION")
2025-03-14T04:05:09.4146079Z fi
2025-03-14T04:05:09.4146323Z 
2025-03-14T04:05:09.4146583Z if [[ -z "${MERGE_BASE}" ]]; then
2025-03-14T04:05:09.4146969Z   echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4147327Z 
2025-03-14T04:05:09.4147822Z   echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..."
2025-03-14T04:05:09.4148409Z   exit 0
2025-03-14T04:05:09.4148658Z fi
2025-03-14T04:05:09.4149226Z 
2025-03-14T04:05:09.4149574Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then
2025-03-14T04:05:09.4150292Z   echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit"
2025-03-14T04:05:09.4150912Z   exit 1
2025-03-14T04:05:09.4151169Z fi
2025-03-14T04:05:09.4151409Z 
2025-03-14T04:05:09.4151807Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}")
2025-03-14T04:05:09.4152494Z # If no image exists but the hash is the same as the previous hash then we should error out here
2025-03-14T04:05:09.4153109Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then
2025-03-14T04:05:09.4153821Z   echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch"
2025-03-14T04:05:09.4154629Z   echo "         Will re-build docker image to store in local cache, TTS may be longer"
2025-03-14T04:05:09.4155117Z fi
2025-03-14T04:05:09.4155338Z 
2025-03-14T04:05:09.4155628Z echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:05:09.4162168Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:09.4162579Z env:
2025-03-14T04:05:09.4162807Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:09.4163111Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-14T04:05:09.4163482Z   BASE_REVISION: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:05:09.4164305Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.4165157Z   DOCKER_TAG: aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:09.4165630Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:09.4166048Z   DOCKER_PUSH: 
2025-03-14T04:05:09.4166316Z ##[endgroup]
2025-03-14T04:05:09.4188546Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:09.4189029Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:09.4191040Z + aws ecr get-login-password --region us-east-1
2025-03-14T04:05:09.4192352Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:09.9465475Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:05:09.9466128Z Configure a credential helper to remove this warning. See
2025-03-14T04:05:09.9466745Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:05:09.9467170Z 
2025-03-14T04:05:09.9467733Z Login Succeeded
2025-03-14T04:05:09.9482131Z ++ date +%s
2025-03-14T04:05:09.9490480Z + START_TIME=1741925109
2025-03-14T04:05:09.9493540Z ++ date +%s
2025-03-14T04:05:09.9501552Z + [[ 1741917909 -lt 1741925109 ]]
2025-03-14T04:05:09.9502405Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:10.1781005Z {
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2025-03-14T04:05:10.1782797Z 	"config": {
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2025-03-14T04:05:10.1784675Z 	},
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2025-03-14T04:05:10.1792496Z 		},
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2025-03-14T04:05:10.1794442Z 		},
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2025-03-14T04:05:10.1796415Z 		},
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2025-03-14T04:05:10.1798415Z 		},
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2025-03-14T04:05:10.1800483Z 		},
2025-03-14T04:05:10.1800706Z 		{
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2025-03-14T04:05:10.1801515Z 			"size": 485,
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2025-03-14T04:05:10.1802451Z 		},
2025-03-14T04:05:10.1802667Z 		{
2025-03-14T04:05:10.1803007Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1803449Z 			"size": 110,
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2025-03-14T04:05:10.1804391Z 		},
2025-03-14T04:05:10.1804613Z 		{
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2025-03-14T04:05:10.1806334Z 		},
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2025-03-14T04:05:10.1808413Z 		},
2025-03-14T04:05:10.1808628Z 		{
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2025-03-14T04:05:10.1809834Z 			"digest": "sha256:8d28e44b71738e9970d281b96b71162fe653767f207b2e9b6fadb1e162a752dd"
2025-03-14T04:05:10.1810322Z 		},
2025-03-14T04:05:10.1810542Z 		{
2025-03-14T04:05:10.1810895Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1811339Z 			"size": 478,
2025-03-14T04:05:10.1811763Z 			"digest": "sha256:0aa1896ea194d7533275930170e3955bdcf031b815ce2e3965435aae2dfcee53"
2025-03-14T04:05:10.1812251Z 		},
2025-03-14T04:05:10.1812466Z 		{
2025-03-14T04:05:10.1812815Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1813257Z 			"size": 2834923920,
2025-03-14T04:05:10.1813797Z 			"digest": "sha256:bd2cd6cca9d3af4e103c9030749de95c65cb0cb342debfdbfdac031a73e8e485"
2025-03-14T04:05:10.1814310Z 		},
2025-03-14T04:05:10.1814527Z 		{
2025-03-14T04:05:10.1814939Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1815417Z 			"size": 32,
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2025-03-14T04:05:10.1816339Z 		},
2025-03-14T04:05:10.1816559Z 		{
2025-03-14T04:05:10.1816883Z + exit 0
2025-03-14T04:05:10.1817245Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1817690Z 			"size": 380,
2025-03-14T04:05:10.1818130Z 			"digest": "sha256:b1f6e7b115fa0e2dd27f4fccc08d501563df2168dd261f073be1596deb42f12f"
2025-03-14T04:05:10.1818637Z 		},
2025-03-14T04:05:10.1818855Z 		{
2025-03-14T04:05:10.1819209Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1819645Z 			"size": 104,
2025-03-14T04:05:10.1820082Z 			"digest": "sha256:fe5bda901c2713f72fc1758c09821e2473398825caf5786dab2685a363156dda"
2025-03-14T04:05:10.1820572Z 		},
2025-03-14T04:05:10.1820788Z 		{
2025-03-14T04:05:10.1821137Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1821574Z 			"size": 231,
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2025-03-14T04:05:10.1822480Z 		},
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2025-03-14T04:05:10.1824368Z 		},
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2025-03-14T04:05:10.1826290Z 		},
2025-03-14T04:05:10.1826507Z 		{
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2025-03-14T04:05:10.1827304Z 			"size": 105,
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2025-03-14T04:05:10.1828246Z 		},
2025-03-14T04:05:10.1828447Z 		{
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2025-03-14T04:05:10.1829235Z 			"size": 802,
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2025-03-14T04:05:10.1830182Z 		},
2025-03-14T04:05:10.1830402Z 		{
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2025-03-14T04:05:10.1831188Z 			"size": 32,
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2025-03-14T04:05:10.1832239Z 		},
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2025-03-14T04:05:10.1834200Z 		},
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2025-03-14T04:05:10.1868471Z 		},
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2025-03-14T04:05:10.1870384Z 		},
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2025-03-14T04:05:10.1871521Z 			"size": 107,
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2025-03-14T04:05:10.1872435Z 		},
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2025-03-14T04:05:10.1873397Z 			"size": 489,
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2025-03-14T04:05:10.1874308Z 		},
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2025-03-14T04:05:10.1876157Z 		},
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2025-03-14T04:05:10.1877992Z 		},
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2025-03-14T04:05:10.1879900Z 		},
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2025-03-14T04:05:10.1881776Z 		},
2025-03-14T04:05:10.1881974Z 		{
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2025-03-14T04:05:10.1882730Z 			"size": 163,
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2025-03-14T04:05:10.1883605Z 		},
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2025-03-14T04:05:10.1885446Z 		},
2025-03-14T04:05:10.1885642Z 		{
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2025-03-14T04:05:10.1973027Z 		{
2025-03-14T04:05:10.1973379Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1973817Z 			"size": 104,
2025-03-14T04:05:10.1974246Z 			"digest": "sha256:ef7783cc3f879a80a8d272eb89621a617d893505c068983041d5a03c3a1a229c"
2025-03-14T04:05:10.1974729Z 		},
2025-03-14T04:05:10.1974943Z 		{
2025-03-14T04:05:10.1975290Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1975721Z 			"size": 1899,
2025-03-14T04:05:10.1976155Z 			"digest": "sha256:c706ce4bc239c216e5dc1cff9a5317045a764fe71946b2fcaba1605c94d4fe44"
2025-03-14T04:05:10.1976646Z 		},
2025-03-14T04:05:10.1976860Z 		{
2025-03-14T04:05:10.1977208Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1977648Z 			"size": 234806516,
2025-03-14T04:05:10.1978084Z 			"digest": "sha256:3cbcaa230a48f1928daeecb346b6983b65fb479e35f88c760297157c89167fbe"
2025-03-14T04:05:10.1978575Z 		},
2025-03-14T04:05:10.1978785Z 		{
2025-03-14T04:05:10.1979134Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1979578Z 			"size": 106,
2025-03-14T04:05:10.1980021Z 			"digest": "sha256:c4467d3184270ce9fbd4ecde8971ba8bb0d9156f3fb300bb29c8e87d32f1bf07"
2025-03-14T04:05:10.1980522Z 		},
2025-03-14T04:05:10.1980742Z 		{
2025-03-14T04:05:10.1981096Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1981530Z 			"size": 165,
2025-03-14T04:05:10.1981964Z 			"digest": "sha256:0e0314e23cf1e0a0e157747a7e37d8296bf23f5d518fbef2d5bf4a8cdba07461"
2025-03-14T04:05:10.1982464Z 		},
2025-03-14T04:05:10.1982681Z 		{
2025-03-14T04:05:10.1983031Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1983467Z 			"size": 7943,
2025-03-14T04:05:10.1983897Z 			"digest": "sha256:358e887b5af9808eae2ce637e95799209ebd8ec45f07fa67fe5a0d8b8a1e71b8"
2025-03-14T04:05:10.1984385Z 		},
2025-03-14T04:05:10.1984571Z 		{
2025-03-14T04:05:10.1984904Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1985337Z 			"size": 8067,
2025-03-14T04:05:10.1985825Z 			"digest": "sha256:11857a6b785113e5d3abc950151f3264e143818f315c332c1a8bb0bca77b6a2c"
2025-03-14T04:05:10.1986315Z 		},
2025-03-14T04:05:10.1986527Z 		{
2025-03-14T04:05:10.1986867Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1987299Z 			"size": 304,
2025-03-14T04:05:10.1987723Z 			"digest": "sha256:8e789f6566610685eddbd59f5579c8da8b23cb7eeff85719ab7633c4bafb4d87"
2025-03-14T04:05:10.1988204Z 		},
2025-03-14T04:05:10.1988406Z 		{
2025-03-14T04:05:10.1988745Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1989177Z 			"size": 32,
2025-03-14T04:05:10.1989600Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:05:10.1990091Z 		},
2025-03-14T04:05:10.1990298Z 		{
2025-03-14T04:05:10.1990628Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1991060Z 			"size": 108,
2025-03-14T04:05:10.1991466Z 			"digest": "sha256:8f116d3e883b024c04e368279121d95f3ee6856318135140ac97daad5cdaf463"
2025-03-14T04:05:10.1991928Z 		},
2025-03-14T04:05:10.1992189Z 		{
2025-03-14T04:05:10.1992530Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1992953Z 			"size": 54145664,
2025-03-14T04:05:10.1993384Z 			"digest": "sha256:b21e2d445d94ba8705ac9bb6100d937a194fe7b439ce5f2c3b40255f0cfed309"
2025-03-14T04:05:10.1993870Z 		},
2025-03-14T04:05:10.1994077Z 		{
2025-03-14T04:05:10.1994417Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:05:10.1994851Z 			"size": 32,
2025-03-14T04:05:10.1995275Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:05:10.1995771Z 		}
2025-03-14T04:05:10.1995975Z 	]
2025-03-14T04:05:10.1996175Z }
2025-03-14T04:05:10.2033248Z ##[group]Run set -eux
2025-03-14T04:05:10.2033565Z set -eux
2025-03-14T04:05:10.2034489Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin
2025-03-14T04:05:10.2041763Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:10.2042173Z env:
2025-03-14T04:05:10.2042421Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:10.2042709Z ##[endgroup]
2025-03-14T04:05:10.2068517Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token
2025-03-14T04:05:10.2069179Z + jq --raw-output .SecretString
2025-03-14T04:05:10.2070467Z + jq -r .docker_hub_readonly_token
2025-03-14T04:05:10.2071532Z + docker login --username pytorchbot --password-stdin
2025-03-14T04:05:10.8366965Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:05:10.8367628Z Configure a credential helper to remove this warning. See
2025-03-14T04:05:10.8368242Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:05:10.8368666Z 
2025-03-14T04:05:10.8368869Z Login Succeeded
2025-03-14T04:05:10.8463081Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/}
2025-03-14T04:05:10.8463504Z tag=${ECR_DOCKER_IMAGE##*/}
2025-03-14T04:05:10.8463925Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
2025-03-14T04:05:10.8469895Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:10.8470304Z env:
2025-03-14T04:05:10.8470548Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:10.8471288Z   ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:10.8472051Z ##[endgroup]
2025-03-14T04:05:10.8496667Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.13-clang10-aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:10.8556067Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2025-03-14T04:05:10.8556534Z with:
2025-03-14T04:05:10.8557216Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:10.8558214Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:10.8558629Z env:
2025-03-14T04:05:10.8558876Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:10.8559253Z ##[endgroup]
2025-03-14T04:05:10.8598139Z ##[group]Run set -x
2025-03-14T04:05:10.8598446Z set -x
2025-03-14T04:05:10.8598711Z set +e
2025-03-14T04:05:10.8598967Z 
2025-03-14T04:05:10.8599298Z login() {
2025-03-14T04:05:10.8599816Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-14T04:05:10.8600369Z }
2025-03-14T04:05:10.8600615Z 
2025-03-14T04:05:10.8600902Z retry () {
2025-03-14T04:05:10.8601211Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-14T04:05:10.8601562Z }
2025-03-14T04:05:10.8601798Z 
2025-03-14T04:05:10.8602068Z retry login "${DOCKER_REGISTRY}"
2025-03-14T04:05:10.8602419Z 
2025-03-14T04:05:10.8602655Z set -e
2025-03-14T04:05:10.8603016Z # ignore output since only exit code is used for conditional
2025-03-14T04:05:10.8603541Z # only pull docker image if it's not available locally
2025-03-14T04:05:10.8604114Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2025-03-14T04:05:10.8604649Z   retry docker pull "${DOCKER_IMAGE}"
2025-03-14T04:05:10.8605002Z fi
2025-03-14T04:05:10.8610382Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:05:10.8610775Z env:
2025-03-14T04:05:10.8611025Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:05:10.8611759Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:10.8612618Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:10.8613049Z ##[endgroup]
2025-03-14T04:05:10.8636495Z + set +e
2025-03-14T04:05:10.8636951Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:10.8637592Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:10.8639724Z + aws ecr get-login-password --region us-east-1
2025-03-14T04:05:10.8640954Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:05:11.3793565Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:05:11.3794350Z Configure a credential helper to remove this warning. See
2025-03-14T04:05:11.3795306Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:05:11.3795841Z 
2025-03-14T04:05:11.3795981Z Login Succeeded
2025-03-14T04:05:11.3806741Z + set -e
2025-03-14T04:05:11.3807680Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:11.3926993Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:11.3928421Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:05:11.7893239Z aa89d6e739080d90fa18625d57297c6734465849: Pulling from pytorch/pytorch-linux-focal-py3.13-clang10
2025-03-14T04:05:11.7894916Z 86e5016c2693: Pulling fs layer
2025-03-14T04:05:11.7895523Z b64eae0f9865: Pulling fs layer
2025-03-14T04:05:11.7896254Z fa6343415b98: Pulling fs layer
2025-03-14T04:05:11.7897044Z 403180670172: Pulling fs layer
2025-03-14T04:05:11.7897735Z 977f9685456c: Pulling fs layer
2025-03-14T04:05:11.7899634Z 0000d4c5345d: Pulling fs layer
2025-03-14T04:05:11.7900205Z cc49a6f5bbf9: Pulling fs layer
2025-03-14T04:05:11.7900934Z 955f46363639: Pulling fs layer
2025-03-14T04:05:11.7901489Z 176fd07a5b37: Pulling fs layer
2025-03-14T04:05:11.7902039Z 14c6878b88e1: Pulling fs layer
2025-03-14T04:05:11.7902513Z 44a2b2adb348: Pulling fs layer
2025-03-14T04:05:11.7902919Z 8d28e44b7173: Pulling fs layer
2025-03-14T04:05:11.7903262Z 0aa1896ea194: Pulling fs layer
2025-03-14T04:05:11.7903722Z bd2cd6cca9d3: Pulling fs layer
2025-03-14T04:05:11.7904129Z 4f4fb700ef54: Pulling fs layer
2025-03-14T04:05:11.7904547Z b1f6e7b115fa: Pulling fs layer
2025-03-14T04:05:11.7904972Z fe5bda901c27: Pulling fs layer
2025-03-14T04:05:11.7905791Z 69d1681b54f4: Pulling fs layer
2025-03-14T04:05:11.7906265Z 4e533c6a25a5: Pulling fs layer
2025-03-14T04:05:11.7906959Z 8b0f88941fa0: Pulling fs layer
2025-03-14T04:05:11.7907668Z 5e8b2a6cebeb: Pulling fs layer
2025-03-14T04:05:11.7923164Z b7fb51dfb04b: Pulling fs layer
2025-03-14T04:05:11.7923533Z 83ec6a063b6f: Pulling fs layer
2025-03-14T04:05:11.7923867Z ffbfe8392dca: Pulling fs layer
2025-03-14T04:05:11.7924173Z 0000d4c5345d: Waiting
2025-03-14T04:05:11.7924459Z 317c2ec95e89: Pulling fs layer
2025-03-14T04:05:11.7924829Z 176fd07a5b37: Waiting
2025-03-14T04:05:11.7925109Z cc49a6f5bbf9: Waiting
2025-03-14T04:05:11.7925434Z 08d87c988dc7: Pulling fs layer
2025-03-14T04:05:11.7925768Z 14c6878b88e1: Waiting
2025-03-14T04:05:11.7926375Z 46a403bf2b51: Pulling fs layer
2025-03-14T04:05:11.7926923Z 955f46363639: Waiting
2025-03-14T04:05:11.7927372Z 567710e9eb36: Pulling fs layer
2025-03-14T04:05:11.7927852Z 44a2b2adb348: Waiting
2025-03-14T04:05:11.7928320Z 8d6eb4d15a1e: Pulling fs layer
2025-03-14T04:05:11.7928827Z 8b0f88941fa0: Waiting
2025-03-14T04:05:11.7929295Z 23e2930ed719: Pulling fs layer
2025-03-14T04:05:11.7929820Z 6775f1cd04ed: Pulling fs layer
2025-03-14T04:05:11.7930342Z 96b0e3c627d1: Pulling fs layer
2025-03-14T04:05:11.7930857Z 169f1b4c983d: Pulling fs layer
2025-03-14T04:05:11.7931239Z 4cda9e9a6e26: Pulling fs layer
2025-03-14T04:05:11.7931557Z 1f9815b2b3b1: Pulling fs layer
2025-03-14T04:05:11.7932011Z fafa7442899b: Pulling fs layer
2025-03-14T04:05:11.7932392Z 8ac10abfa1af: Pulling fs layer
2025-03-14T04:05:11.7932720Z b7fb51dfb04b: Waiting
2025-03-14T04:05:11.7933022Z 07916ca736e8: Pulling fs layer
2025-03-14T04:05:11.7933432Z 83ec6a063b6f: Waiting
2025-03-14T04:05:11.7933758Z ffbfe8392dca: Waiting
2025-03-14T04:05:11.7934230Z 317c2ec95e89: Waiting
2025-03-14T04:05:11.7934691Z 426b4781db6d: Pulling fs layer
2025-03-14T04:05:11.7935199Z 08d87c988dc7: Waiting
2025-03-14T04:05:11.7935695Z 27916e7dfc6b: Pulling fs layer
2025-03-14T04:05:11.7936095Z f3d689fa4b30: Pulling fs layer
2025-03-14T04:05:11.7936418Z b3611088a559: Pulling fs layer
2025-03-14T04:05:11.7936830Z 766763116b65: Pulling fs layer
2025-03-14T04:05:11.7937141Z b1f6e7b115fa: Waiting
2025-03-14T04:05:11.7937592Z fe5bda901c27: Waiting
2025-03-14T04:05:11.7937984Z 0e592153fa17: Pulling fs layer
2025-03-14T04:05:11.7938354Z 403180670172: Waiting
2025-03-14T04:05:11.7938623Z 96b0e3c627d1: Waiting
2025-03-14T04:05:11.7938912Z d98195448b21: Pulling fs layer
2025-03-14T04:05:11.7939205Z 169f1b4c983d: Waiting
2025-03-14T04:05:11.7939484Z 567710e9eb36: Waiting
2025-03-14T04:05:11.7939770Z 69d1681b54f4: Waiting
2025-03-14T04:05:11.7940038Z 8d28e44b7173: Waiting
2025-03-14T04:05:11.7940332Z 4cda9e9a6e26: Waiting
2025-03-14T04:05:11.7940624Z 4e533c6a25a5: Waiting
2025-03-14T04:05:11.7961540Z 977f9685456c: Waiting
2025-03-14T04:05:11.7961907Z 11b77c5e6a4c: Pulling fs layer
2025-03-14T04:05:11.7962191Z 6775f1cd04ed: Waiting
2025-03-14T04:05:11.7962443Z 09894530ccd2: Pulling fs layer
2025-03-14T04:05:11.7962714Z f3d689fa4b30: Waiting
2025-03-14T04:05:11.7962951Z 23e2930ed719: Waiting
2025-03-14T04:05:11.7963189Z 8d6eb4d15a1e: Waiting
2025-03-14T04:05:11.7963422Z 766763116b65: Waiting
2025-03-14T04:05:11.7963664Z dd81d8b11712: Pulling fs layer
2025-03-14T04:05:11.7963933Z 426b4781db6d: Waiting
2025-03-14T04:05:11.7964402Z 61f09cfa492b: Pulling fs layer
2025-03-14T04:05:11.7964676Z 0aa1896ea194: Waiting
2025-03-14T04:05:11.7964909Z 4f4fb700ef54: Waiting
2025-03-14T04:05:11.7965153Z 27916e7dfc6b: Waiting
2025-03-14T04:05:11.7965389Z 5e8b2a6cebeb: Waiting
2025-03-14T04:05:11.7965729Z d98195448b21: Waiting
2025-03-14T04:05:11.7965979Z 37f50b3c6dc5: Pulling fs layer
2025-03-14T04:05:11.7966244Z 09894530ccd2: Waiting
2025-03-14T04:05:11.7966476Z dd81d8b11712: Waiting
2025-03-14T04:05:11.7966710Z bd2cd6cca9d3: Waiting
2025-03-14T04:05:11.7966958Z f4222d90a3b0: Pulling fs layer
2025-03-14T04:05:11.7967224Z 1f9815b2b3b1: Waiting
2025-03-14T04:05:11.7967452Z b3611088a559: Waiting
2025-03-14T04:05:11.7967695Z f0e09b868978: Pulling fs layer
2025-03-14T04:05:11.7967962Z 8ac10abfa1af: Waiting
2025-03-14T04:05:11.7968197Z 37f50b3c6dc5: Waiting
2025-03-14T04:05:11.7968427Z f4222d90a3b0: Waiting
2025-03-14T04:05:11.7968654Z f0e09b868978: Waiting
2025-03-14T04:05:11.7968899Z fea32805da6a: Pulling fs layer
2025-03-14T04:05:11.7969166Z fea32805da6a: Waiting
2025-03-14T04:05:11.7969419Z e88ebc603e68: Pulling fs layer
2025-03-14T04:05:11.7969689Z 11b77c5e6a4c: Waiting
2025-03-14T04:05:11.7969931Z 127286786396: Pulling fs layer
2025-03-14T04:05:11.7970193Z e88ebc603e68: Waiting
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2025-03-14T04:05:11.7970654Z 127286786396: Waiting
2025-03-14T04:05:11.7970901Z edffc976b12a: Pulling fs layer
2025-03-14T04:05:11.7971174Z 61f09cfa492b: Waiting
2025-03-14T04:05:11.7971424Z a9b1becf3ab8: Pulling fs layer
2025-03-14T04:05:11.7971708Z 8d150e46d80d: Pulling fs layer
2025-03-14T04:05:11.7971980Z fafa7442899b: Waiting
2025-03-14T04:05:11.7972222Z a9b1becf3ab8: Waiting
2025-03-14T04:05:11.7972462Z edffc976b12a: Waiting
2025-03-14T04:05:11.7972715Z 702b72d020bb: Pulling fs layer
2025-03-14T04:05:11.7972985Z 0e592153fa17: Waiting
2025-03-14T04:05:11.7973231Z 21479af96a39: Pulling fs layer
2025-03-14T04:05:11.7973502Z 8d150e46d80d: Waiting
2025-03-14T04:05:11.7973766Z 702b72d020bb: Waiting
2025-03-14T04:05:11.7974023Z 21479af96a39: Waiting
2025-03-14T04:05:11.7974298Z ea88b7068e4f: Pulling fs layer
2025-03-14T04:05:11.7974607Z 78b28fc0ca82: Pulling fs layer
2025-03-14T04:05:11.7974898Z ef7783cc3f87: Pulling fs layer
2025-03-14T04:05:11.7975205Z c706ce4bc239: Pulling fs layer
2025-03-14T04:05:11.7975517Z 3cbcaa230a48: Pulling fs layer
2025-03-14T04:05:11.7975814Z ea88b7068e4f: Waiting
2025-03-14T04:05:11.7976080Z c706ce4bc239: Waiting
2025-03-14T04:05:11.7976351Z c4467d318427: Pulling fs layer
2025-03-14T04:05:11.7976654Z 0e0314e23cf1: Pulling fs layer
2025-03-14T04:05:11.7976937Z ef7783cc3f87: Waiting
2025-03-14T04:05:11.7977193Z 358e887b5af9: Pulling fs layer
2025-03-14T04:05:11.7977475Z 11857a6b7851: Pulling fs layer
2025-03-14T04:05:11.7977751Z 8e789f656661: Pulling fs layer
2025-03-14T04:05:11.7978021Z 358e887b5af9: Waiting
2025-03-14T04:05:11.7978261Z 8e789f656661: Waiting
2025-03-14T04:05:11.7978501Z 3cbcaa230a48: Waiting
2025-03-14T04:05:11.7978756Z 8f116d3e883b: Pulling fs layer
2025-03-14T04:05:11.7979023Z 0e0314e23cf1: Waiting
2025-03-14T04:05:11.7979282Z b21e2d445d94: Pulling fs layer
2025-03-14T04:05:11.7979553Z b21e2d445d94: Waiting
2025-03-14T04:05:11.7979794Z 8f116d3e883b: Waiting
2025-03-14T04:05:11.8881012Z b64eae0f9865: Verifying Checksum
2025-03-14T04:05:11.8881742Z b64eae0f9865: Download complete
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2025-03-14T04:05:12.1198970Z 86e5016c2693: Download complete
2025-03-14T04:05:12.2058640Z 0000d4c5345d: Verifying Checksum
2025-03-14T04:05:12.2059633Z 0000d4c5345d: Download complete
2025-03-14T04:05:12.2998558Z cc49a6f5bbf9: Verifying Checksum
2025-03-14T04:05:12.2999378Z cc49a6f5bbf9: Download complete
2025-03-14T04:05:12.3715177Z 955f46363639: Download complete
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2025-03-14T04:05:12.5486720Z 14c6878b88e1: Verifying Checksum
2025-03-14T04:05:12.5487390Z 14c6878b88e1: Download complete
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2025-03-14T04:05:12.7140081Z 8d28e44b7173: Verifying Checksum
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2025-03-14T04:05:13.1041152Z 69d1681b54f4: Download complete
2025-03-14T04:05:13.1424386Z 86e5016c2693: Pull complete
2025-03-14T04:05:13.2028015Z 4e533c6a25a5: Verifying Checksum
2025-03-14T04:05:13.2028563Z 4e533c6a25a5: Download complete
2025-03-14T04:05:13.3090894Z 8b0f88941fa0: Verifying Checksum
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2025-03-14T04:05:13.4004655Z 5e8b2a6cebeb: Verifying Checksum
2025-03-14T04:05:13.4005360Z 5e8b2a6cebeb: Download complete
2025-03-14T04:05:13.4457387Z b64eae0f9865: Pull complete
2025-03-14T04:05:13.4768782Z b7fb51dfb04b: Verifying Checksum
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2025-03-14T04:05:15.3947509Z 169f1b4c983d: Verifying Checksum
2025-03-14T04:05:15.3948205Z 169f1b4c983d: Download complete
2025-03-14T04:05:15.4922267Z 4cda9e9a6e26: Verifying Checksum
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2025-03-14T04:05:15.5677740Z 1f9815b2b3b1: Verifying Checksum
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2025-03-14T04:05:20.0066832Z 8f116d3e883b: Verifying Checksum
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2025-03-14T04:05:21.0428315Z 3cbcaa230a48: Verifying Checksum
2025-03-14T04:05:21.0428739Z 3cbcaa230a48: Download complete
2025-03-14T04:05:22.8991610Z fa6343415b98: Pull complete
2025-03-14T04:05:23.0487906Z 403180670172: Pull complete
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2025-03-14T04:05:25.6946224Z 0aa1896ea194: Pull complete
2025-03-14T04:05:55.8645266Z bd2cd6cca9d3: Verifying Checksum
2025-03-14T04:05:55.8645671Z bd2cd6cca9d3: Download complete
2025-03-14T04:06:34.6872305Z bd2cd6cca9d3: Pull complete
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2025-03-14T04:07:01.9510486Z Digest: sha256:f3d6a3edfcad1af24cc8a256a5d3fd25be79458006d512d917d0ff8916c757ad
2025-03-14T04:07:01.9991096Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:07:02.0258118Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:07:02.0303598Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:07:02.0304597Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:07:02.0312453Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:02.0312861Z env:
2025-03-14T04:07:02.0313103Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:02.0313400Z ##[endgroup]
2025-03-14T04:07:02.0402512Z Prepare all required actions
2025-03-14T04:07:02.0561008Z ##[group]Run ./.github/actions/get-workflow-job-id
2025-03-14T04:07:02.0561384Z with:
2025-03-14T04:07:02.0561826Z   github-token: ***
2025-03-14T04:07:02.0562085Z env:
2025-03-14T04:07:02.0562324Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:02.0562774Z ##[endgroup]
2025-03-14T04:07:02.0681632Z ##[group]Run set -eux
2025-03-14T04:07:02.0682005Z set -eux
2025-03-14T04:07:02.0682479Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-14T04:07:02.0688372Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:02.0688784Z env:
2025-03-14T04:07:02.0689038Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:02.0689597Z   GITHUB_TOKEN: ***
2025-03-14T04:07:02.0689872Z ##[endgroup]
2025-03-14T04:07:02.0713099Z + python3 .github/scripts/get_workflow_job_id.py 13849515290 i-01e2d8c57795e0761
2025-03-14T04:07:03.0737973Z setting job-id=38754598997
2025-03-14T04:07:03.0738591Z setting job-name=linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:03.0992150Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-14T04:07:03.0992924Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-14T04:07:03.0993541Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2025-03-14T04:07:03.0994048Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:07:03.1000547Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:03.1000959Z env:
2025-03-14T04:07:03.1001202Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:03.1001493Z   JOB_ID: 38754598997
2025-03-14T04:07:03.1001911Z   JOB_NAME: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:03.1002385Z   WORKFLOW_NAME: pull
2025-03-14T04:07:03.1002660Z   WORKFLOW_RUN_ID: 13849515290
2025-03-14T04:07:03.1002946Z ##[endgroup]
2025-03-14T04:07:03.3370151Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T04:07:03.3536020Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1)
2025-03-14T04:07:03.3540547Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84)
2025-03-14T04:07:03.3544788Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7)
2025-03-14T04:07:03.3647358Z Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (3.26.1)
2025-03-14T04:07:03.3653203Z Requirement already satisfied: typing-inspect<1,>=0.4.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (0.9.0)
2025-03-14T04:07:03.3736492Z Requirement already satisfied: packaging>=17.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses_json==0.6.7) (24.2)
2025-03-14T04:07:03.3765963Z Requirement already satisfied: mypy-extensions>=0.3.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (1.0.0)
2025-03-14T04:07:03.3770012Z Requirement already satisfied: typing-extensions>=3.7.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (4.12.2)
2025-03-14T04:07:03.5689385Z Prepare all required actions
2025-03-14T04:07:03.5689827Z Getting action download info
2025-03-14T04:07:03.6704645Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6)
2025-03-14T04:07:04.2630983Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806)
2025-03-14T04:07:06.1065766Z ##[group]Run ./.github/actions/download-build-artifacts
2025-03-14T04:07:06.1066169Z with:
2025-03-14T04:07:06.1066436Z   name: linux-focal-py3.13-clang10
2025-03-14T04:07:06.1066772Z   s3-bucket: gha-artifacts
2025-03-14T04:07:06.1067060Z env:
2025-03-14T04:07:06.1067300Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:06.1067583Z ##[endgroup]
2025-03-14T04:07:06.1205665Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-14T04:07:06.1206050Z with:
2025-03-14T04:07:06.1206467Z   name: linux-focal-py3.13-clang10
2025-03-14T04:07:06.1206815Z   s3-bucket: gha-artifacts
2025-03-14T04:07:06.1207163Z   region: us-east-1
2025-03-14T04:07:06.1207417Z env:
2025-03-14T04:07:06.1207654Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:06.1207946Z ##[endgroup]
2025-03-14T04:07:06.9429160Z (node:66057) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-14T04:07:06.9429718Z 
2025-03-14T04:07:06.9429938Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-14T04:07:06.9430498Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-14T04:07:06.9431099Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-14T04:07:07.0515648Z Found 1 objects with prefix pytorch/pytorch/13849515290/linux-focal-py3.13-clang10/
2025-03-14T04:07:07.0516681Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-14T04:07:14.5596568Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-14T04:07:14.5602665Z Artifact download has finished successfully
2025-03-14T04:07:14.5761912Z ##[group]Run unzip -o artifacts.zip
2025-03-14T04:07:14.5762286Z unzip -o artifacts.zip
2025-03-14T04:07:14.5768631Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:14.5769040Z env:
2025-03-14T04:07:14.5769287Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:14.5769580Z ##[endgroup]
2025-03-14T04:07:14.5930272Z Archive:  artifacts.zip
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2025-03-14T04:07:15.5818626Z   inflating: dist/.ninja_log         
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2025-03-14T04:07:21.2037293Z   inflating: .additional_ci_files/test-class-times.json  
2025-03-14T04:07:21.2068490Z ##[group]Run rm artifacts.zip
2025-03-14T04:07:21.2068836Z rm artifacts.zip
2025-03-14T04:07:21.2074562Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:21.2074974Z env:
2025-03-14T04:07:21.2075365Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:21.2075661Z ##[endgroup]
2025-03-14T04:07:21.2351581Z ##[group]Run df -H
2025-03-14T04:07:21.2351890Z df -H
2025-03-14T04:07:21.2357518Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:21.2357930Z env:
2025-03-14T04:07:21.2358173Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:21.2358463Z ##[endgroup]
2025-03-14T04:07:21.2409688Z Filesystem        Size  Used Avail Use% Mounted on
2025-03-14T04:07:21.2410113Z devtmpfs          4.2M     0  4.2M   0% /dev
2025-03-14T04:07:21.2410489Z tmpfs              17G   29k   17G   1% /dev/shm
2025-03-14T04:07:21.2410854Z tmpfs             6.6G  521k  6.6G   1% /run
2025-03-14T04:07:21.2411193Z /dev/nvme0n1p1    161G   26G  136G  16% /
2025-03-14T04:07:21.2411685Z tmpfs              17G   25k   17G   1% /tmp
2025-03-14T04:07:21.2412067Z /dev/nvme0n1p128   11M  1.4M  9.2M  13% /boot/efi
2025-03-14T04:07:21.2445016Z Prepare all required actions
2025-03-14T04:07:21.2445436Z Getting action download info
2025-03-14T04:07:21.3595288Z ##[group]Run ./.github/actions/download-td-artifacts
2025-03-14T04:07:21.3595681Z with:
2025-03-14T04:07:21.3595914Z env:
2025-03-14T04:07:21.3596145Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:21.3596431Z ##[endgroup]
2025-03-14T04:07:21.3630451Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-14T04:07:21.3630829Z with:
2025-03-14T04:07:21.3631068Z   name: td_results
2025-03-14T04:07:21.3631339Z   s3-bucket: gha-artifacts
2025-03-14T04:07:21.3631633Z   region: us-east-1
2025-03-14T04:07:21.3631884Z env:
2025-03-14T04:07:21.3632118Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:21.3632403Z ##[endgroup]
2025-03-14T04:07:21.8183417Z (node:66078) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-14T04:07:21.8183951Z 
2025-03-14T04:07:21.8184237Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-14T04:07:21.8184820Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-14T04:07:21.8185432Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-14T04:07:21.9148098Z Found 1 objects with prefix pytorch/pytorch/13849515290/td_results/
2025-03-14T04:07:21.9148987Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json
2025-03-14T04:07:21.9708962Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json
2025-03-14T04:07:21.9713737Z Artifact download has finished successfully
2025-03-14T04:07:21.9857665Z ##[group]Run mkdir -p .additional_ci_files
2025-03-14T04:07:21.9858079Z mkdir -p .additional_ci_files
2025-03-14T04:07:21.9858550Z mv td_results.json .additional_ci_files/td_results.json || true
2025-03-14T04:07:21.9864355Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:21.9864763Z env:
2025-03-14T04:07:21.9865033Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:21.9865323Z ##[endgroup]
2025-03-14T04:07:21.9966794Z ##[group]Run .github/scripts/parse_ref.py
2025-03-14T04:07:21.9967233Z .github/scripts/parse_ref.py
2025-03-14T04:07:21.9972774Z shell: /usr/bin/bash -e {0}
2025-03-14T04:07:21.9973063Z env:
2025-03-14T04:07:21.9973307Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:21.9973598Z ##[endgroup]
2025-03-14T04:07:22.0386658Z Prepare all required actions
2025-03-14T04:07:22.0387979Z Getting action download info
2025-03-14T04:07:22.1561107Z ##[group]Run ./.github/actions/filter-test-configs
2025-03-14T04:07:22.1561496Z with:
2025-03-14T04:07:22.1561934Z   github-token: ***
2025-03-14T04:07:22.1564683Z   test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}
2025-03-14T04:07:22.1567734Z   job-name: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:22.1568215Z env:
2025-03-14T04:07:22.1568465Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:22.1568758Z ##[endgroup]
2025-03-14T04:07:22.1626411Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T04:07:22.1626751Z with:
2025-03-14T04:07:22.1626971Z   shell: bash
2025-03-14T04:07:22.1627226Z   timeout_minutes: 10
2025-03-14T04:07:22.1627499Z   max_attempts: 5
2025-03-14T04:07:22.1627763Z   retry_wait_seconds: 30
2025-03-14T04:07:22.1628596Z   command: set -eux
# PyYAML 6.0 doesn't work with MacOS x86 anymore
# This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2
python3 -m pip install requests==2.27.1 pyyaml==6.0.1

2025-03-14T04:07:22.1629478Z   polling_interval_seconds: 1
2025-03-14T04:07:22.1629800Z   warning_on_retry: true
2025-03-14T04:07:22.1630092Z   continue_on_error: false
2025-03-14T04:07:22.1630376Z env:
2025-03-14T04:07:22.1630613Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:22.1631186Z   GITHUB_TOKEN: ***
2025-03-14T04:07:22.1631455Z ##[endgroup]
2025-03-14T04:07:22.2542053Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1
2025-03-14T04:07:22.4874019Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T04:07:22.5026177Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1)
2025-03-14T04:07:22.5031168Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1)
2025-03-14T04:07:22.5144922Z Requirement already satisfied: charset-normalizer~=2.0.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2.0.12)
2025-03-14T04:07:22.5153182Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10)
2025-03-14T04:07:22.5157242Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2025.1.31)
2025-03-14T04:07:22.5163018Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10)
2025-03-14T04:07:23.2385525Z Command completed after 1 attempt(s).
2025-03-14T04:07:23.2436272Z ##[group]Run set -x
2025-03-14T04:07:23.2436575Z set -x
2025-03-14T04:07:23.2436831Z 
2025-03-14T04:07:23.2437250Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-14T04:07:23.2437767Z # in runner workspace
2025-03-14T04:07:23.2438206Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py"
2025-03-14T04:07:23.2444276Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:23.2444683Z env:
2025-03-14T04:07:23.2444929Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:23.2445398Z ##[endgroup]
2025-03-14T04:07:23.2469272Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py
2025-03-14T04:07:23.2699880Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-14T04:07:23.2700340Z echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-14T04:07:23.2700719Z echo "Job name: ${JOB_NAME}"
2025-03-14T04:07:23.2701044Z 
2025-03-14T04:07:23.2701454Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-14T04:07:23.2701965Z # in runner workspace
2025-03-14T04:07:23.2702427Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \
2025-03-14T04:07:23.2703075Z   --workflow "${GITHUB_WORKFLOW}" \
2025-03-14T04:07:23.2703445Z   --job-name "${JOB_NAME}" \
2025-03-14T04:07:23.2706226Z   --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}" \
2025-03-14T04:07:23.2709035Z   --selected-test-configs "" \
2025-03-14T04:07:23.2709399Z   --pr-number "${PR_NUMBER}" \
2025-03-14T04:07:23.2709730Z   --tag "${TAG}" \
2025-03-14T04:07:23.2710055Z   --event-name "${EVENT_NAME}" \
2025-03-14T04:07:23.2710409Z   --schedule "${SCHEDULE}" \
2025-03-14T04:07:23.2710749Z   --branch "${HEAD_BRANCH}"
2025-03-14T04:07:23.2716409Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:23.2716812Z env:
2025-03-14T04:07:23.2717065Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:23.2717546Z   GITHUB_TOKEN: ***
2025-03-14T04:07:23.2717970Z   JOB_NAME: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:23.2718454Z   PR_NUMBER: 
2025-03-14T04:07:23.2718703Z   TAG: 
2025-03-14T04:07:23.2718940Z   EVENT_NAME: push
2025-03-14T04:07:23.2719298Z   SCHEDULE: 
2025-03-14T04:07:23.2719542Z   HEAD_BRANCH: 
2025-03-14T04:07:23.2719796Z ##[endgroup]
2025-03-14T04:07:23.2742404Z Workflow: pull
2025-03-14T04:07:23.2742846Z Job name: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:23.4662222Z ##[group]Run echo "Filtered matrix:"
2025-03-14T04:07:23.4662605Z echo "Filtered matrix:"
2025-03-14T04:07:23.4665360Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}"
2025-03-14T04:07:23.4668148Z 
2025-03-14T04:07:23.4668384Z echo
2025-03-14T04:07:23.4668849Z echo "Is the current job unstable? False"
2025-03-14T04:07:23.4669225Z 
2025-03-14T04:07:23.4669440Z echo
2025-03-14T04:07:23.4669727Z echo "Is keep-going label set? False"
2025-03-14T04:07:23.4670083Z 
2025-03-14T04:07:23.4670317Z echo
2025-03-14T04:07:23.4670582Z echo "Renabled issues? "
2025-03-14T04:07:23.4676322Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:23.4676729Z env:
2025-03-14T04:07:23.4676978Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:23.4677270Z ##[endgroup]
2025-03-14T04:07:23.4700005Z Filtered matrix:
2025-03-14T04:07:23.4703173Z {include: [{config: default, shard: 1, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: lf.linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: lf.linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: lf.linux.2xlarge}]}
2025-03-14T04:07:23.4705875Z 
2025-03-14T04:07:23.4706010Z Is the current job unstable? False
2025-03-14T04:07:23.4706239Z 
2025-03-14T04:07:23.4706358Z Is keep-going label set? False
2025-03-14T04:07:23.4706570Z 
2025-03-14T04:07:23.4706673Z Renabled issues? 
2025-03-14T04:07:23.4747122Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-14T04:07:23.4747700Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-14T04:07:23.4753404Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:07:23.4753814Z env:
2025-03-14T04:07:23.4754063Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:23.4754361Z   JOB_TIMEOUT: 600
2025-03-14T04:07:23.4754611Z ##[endgroup]
2025-03-14T04:07:23.4821344Z ##[group]Run set -x
2025-03-14T04:07:23.4821738Z set -x
2025-03-14T04:07:23.4822004Z 
2025-03-14T04:07:23.4822294Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2025-03-14T04:07:23.4822729Z   TEST_COMMAND=.ci/pytorch/multigpu-test.sh
2025-03-14T04:07:23.4823167Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2025-03-14T04:07:23.4823567Z   TEST_COMMAND=.ci/onnx/test.sh
2025-03-14T04:07:23.4823908Z else
2025-03-14T04:07:23.4824196Z   TEST_COMMAND=.ci/pytorch/test.sh
2025-03-14T04:07:23.4824526Z fi
2025-03-14T04:07:23.4824767Z 
2025-03-14T04:07:23.4825077Z # Leaving 1GB for the runner and other things
2025-03-14T04:07:23.4825694Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo)
2025-03-14T04:07:23.4826621Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap
2025-03-14T04:07:23.4827361Z # comes from https://github.com/pytorch/test-infra/pull/6058
2025-03-14T04:07:23.4827927Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3))
2025-03-14T04:07:23.4828377Z 
2025-03-14T04:07:23.4828672Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-14T04:07:23.4829052Z   SHM_OPTS=
2025-03-14T04:07:23.4829330Z   JENKINS_USER=
2025-03-14T04:07:23.4829709Z   # ensure that docker container cleanly exits in 12 hours
2025-03-14T04:07:23.4830206Z   # if for some reason cleanup action doesn't stop container
2025-03-14T04:07:23.4830640Z   # when job is cancelled
2025-03-14T04:07:23.4830979Z   DOCKER_SHELL_CMD="sleep 12h"
2025-03-14T04:07:23.4831304Z 
2025-03-14T04:07:23.4831700Z   # since some steps are skipped on s390x, if they are necessary, run them here
2025-03-14T04:07:23.4832274Z   env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:07:23.4832749Z   env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:07:23.4833130Z else
2025-03-14T04:07:23.4833412Z   SHM_OPTS="--shm-size=${SHM_SIZE}"
2025-03-14T04:07:23.4833781Z   JENKINS_USER="--user jenkins"
2025-03-14T04:07:23.4834124Z   DOCKER_SHELL_CMD=
2025-03-14T04:07:23.4834414Z fi
2025-03-14T04:07:23.4834647Z 
2025-03-14T04:07:23.4835015Z # detached container should get cleaned up by teardown_ec2_linux
2025-03-14T04:07:23.4835587Z # TODO: Stop building test binaries as part of the build phase
2025-03-14T04:07:23.4836378Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice
2025-03-14T04:07:23.4836945Z # shellcheck disable=SC2086,SC2090
2025-03-14T04:07:23.4837315Z container_name=$(docker run \
2025-03-14T04:07:23.4837663Z   ${GPU_FLAG:-} \
2025-03-14T04:07:23.4838009Z   ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
2025-03-14T04:07:23.4838385Z   -e BUILD_ENVIRONMENT \
2025-03-14T04:07:23.4838711Z   -e PR_NUMBER \
2025-03-14T04:07:23.4839016Z   -e GITHUB_ACTIONS \
2025-03-14T04:07:23.4839424Z   -e GITHUB_REPOSITORY \
2025-03-14T04:07:23.4839758Z   -e GITHUB_WORKFLOW \
2025-03-14T04:07:23.4840078Z   -e GITHUB_JOB \
2025-03-14T04:07:23.4840385Z   -e GITHUB_RUN_ID \
2025-03-14T04:07:23.4840703Z   -e GITHUB_RUN_NUMBER \
2025-03-14T04:07:23.4841032Z   -e GITHUB_RUN_ATTEMPT \
2025-03-14T04:07:23.4841355Z   -e JOB_ID \
2025-03-14T04:07:23.4841647Z   -e JOB_NAME \
2025-03-14T04:07:23.4841936Z   -e BASE_SHA \
2025-03-14T04:07:23.4842220Z   -e BRANCH \
2025-03-14T04:07:23.4842498Z   -e SHA1 \
2025-03-14T04:07:23.4842782Z   -e AWS_DEFAULT_REGION \
2025-03-14T04:07:23.4843224Z   -e IN_WHEEL_TEST \
2025-03-14T04:07:23.4843540Z   -e SHARD_NUMBER \
2025-03-14T04:07:23.4843850Z   -e TEST_CONFIG \
2025-03-14T04:07:23.4844160Z   -e NUM_TEST_SHARDS \
2025-03-14T04:07:23.4844481Z   -e REENABLED_ISSUES \
2025-03-14T04:07:23.4844820Z   -e CONTINUE_THROUGH_ERROR \
2025-03-14T04:07:23.4845149Z   -e VERBOSE_TEST_LOGS \
2025-03-14T04:07:23.4845472Z   -e TEST_SHOWLOCALS \
2025-03-14T04:07:23.4845789Z   -e NO_TEST_TIMEOUT \
2025-03-14T04:07:23.4846092Z   -e NO_TD \
2025-03-14T04:07:23.4846374Z   -e TD_DISTRIBUTED \
2025-03-14T04:07:23.4846684Z   -e PR_LABELS \
2025-03-14T04:07:23.4847012Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2025-03-14T04:07:23.4847371Z   -e SCCACHE_BUCKET \
2025-03-14T04:07:23.4847681Z   -e SCCACHE_REGION \
2025-03-14T04:07:23.4847984Z   -e XLA_CUDA \
2025-03-14T04:07:23.4848301Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2025-03-14T04:07:23.4848916Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2025-03-14T04:07:23.4849325Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2025-03-14T04:07:23.4849725Z   -e SKIP_SCCACHE_INITIALIZATION=1 \
2025-03-14T04:07:23.4850170Z   -e HUGGING_FACE_HUB_TOKEN \
2025-03-14T04:07:23.4850558Z   -e SCRIBE_GRAPHQL_ACCESS_TOKEN \
2025-03-14T04:07:23.4850911Z   -e DASHBOARD_TAG \
2025-03-14T04:07:23.4851224Z   -e IS_A100_RUNNER \
2025-03-14T04:07:23.4851545Z   -e ARTIFACTS_FILE_SUFFIX \
2025-03-14T04:07:23.4851944Z   --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \
2025-03-14T04:07:23.4852386Z   --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \
2025-03-14T04:07:23.4852831Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2025-03-14T04:07:23.4853259Z   --security-opt seccomp=unconfined \
2025-03-14T04:07:23.4853636Z   --cap-add=SYS_PTRACE \
2025-03-14T04:07:23.4853963Z   --ipc=host \
2025-03-14T04:07:23.4854260Z   ${SHM_OPTS} \
2025-03-14T04:07:23.4854546Z   --tty \
2025-03-14T04:07:23.4854813Z   --detach \
2025-03-14T04:07:23.4855113Z   --name="${container_name}" \
2025-03-14T04:07:23.4855457Z   ${JENKINS_USER} \
2025-03-14T04:07:23.4855835Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2025-03-14T04:07:23.4856270Z   -w /var/lib/jenkins/workspace \
2025-03-14T04:07:23.4856626Z   "${DOCKER_IMAGE}" \
2025-03-14T04:07:23.4856941Z   ${DOCKER_SHELL_CMD}
2025-03-14T04:07:23.4857238Z )
2025-03-14T04:07:23.4857559Z # Propagate download.pytorch.org IP to container
2025-03-14T04:07:23.4858382Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts"
2025-03-14T04:07:23.4859136Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2025-03-14T04:07:23.4859589Z 
2025-03-14T04:07:23.4859888Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-14T04:07:23.4860501Z   docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt"
2025-03-14T04:07:23.4861052Z fi
2025-03-14T04:07:23.4861287Z 
2025-03-14T04:07:23.4861805Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2025-03-14T04:07:23.4867709Z shell: /usr/bin/bash -e {0}
2025-03-14T04:07:23.4868012Z env:
2025-03-14T04:07:23.4868243Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:07:23.4868592Z   BUILD_ENVIRONMENT: linux-focal-py3.13-clang10
2025-03-14T04:07:23.4868964Z   PR_NUMBER: 
2025-03-14T04:07:23.4869238Z   GITHUB_REPOSITORY: pytorch/pytorch
2025-03-14T04:07:23.4869576Z   GITHUB_WORKFLOW: pull
2025-03-14T04:07:23.4869855Z   GITHUB_JOB: test
2025-03-14T04:07:23.4870129Z   GITHUB_RUN_ID: 13849515290
2025-03-14T04:07:23.4870440Z   GITHUB_RUN_NUMBER: 299021
2025-03-14T04:07:23.4870859Z   GITHUB_RUN_ATTEMPT: 1
2025-03-14T04:07:23.4871141Z   JOB_ID: 38754598997
2025-03-14T04:07:23.4871566Z   JOB_NAME: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:23.4872048Z   BRANCH: main
2025-03-14T04:07:23.4872340Z   SHA1: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:23.4872742Z   BASE_SHA: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:23.4873108Z   TEST_CONFIG: default
2025-03-14T04:07:23.4873385Z   SHARD_NUMBER: 1
2025-03-14T04:07:23.4873644Z   NUM_TEST_SHARDS: 5
2025-03-14T04:07:23.4873899Z   REENABLED_ISSUES: 
2025-03-14T04:07:23.4874185Z   CONTINUE_THROUGH_ERROR: False
2025-03-14T04:07:23.4874508Z   VERBOSE_TEST_LOGS: False
2025-03-14T04:07:23.4874804Z   TEST_SHOWLOCALS: False
2025-03-14T04:07:23.4875092Z   NO_TEST_TIMEOUT: False
2025-03-14T04:07:23.4875369Z   NO_TD: False
2025-03-14T04:07:23.4875624Z   TD_DISTRIBUTED: False
2025-03-14T04:07:23.4875967Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2025-03-14T04:07:23.4876359Z   SCCACHE_REGION: us-east-1
2025-03-14T04:07:23.4876647Z   SHM_SIZE: 1g
2025-03-14T04:07:23.4877330Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:07:23.4878078Z   XLA_CUDA: 
2025-03-14T04:07:23.4878464Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:07:23.4878949Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2025-03-14T04:07:23.4879392Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2025-03-14T04:07:23.4879724Z   DASHBOARD_TAG: 
2025-03-14T04:07:23.4880189Z   HUGGING_FACE_HUB_TOKEN: ***
2025-03-14T04:07:23.4880640Z   SCRIBE_GRAPHQL_ACCESS_TOKEN: ***
2025-03-14T04:07:23.4880965Z   IS_A100_RUNNER: 0
2025-03-14T04:07:23.4881356Z   ARTIFACTS_FILE_SUFFIX: test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T04:07:23.4881802Z ##[endgroup]
2025-03-14T04:07:23.4904346Z + [[ default == \m\u\l\t\i\g\p\u ]]
2025-03-14T04:07:23.4904737Z + [[ linux-focal-py3.13-clang10 == *onnx* ]]
2025-03-14T04:07:23.4905108Z + TEST_COMMAND=.ci/pytorch/test.sh
2025-03-14T04:07:23.4907469Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo
2025-03-14T04:07:23.4925046Z + TOTAL_AVAILABLE_MEMORY_IN_GB='29.641 '
2025-03-14T04:07:23.4925442Z + TOTAL_MEMORY_WITH_SWAP=32
2025-03-14T04:07:23.4925807Z + [[ linux-focal-py3.13-clang10 == *\s\3\9\0\x* ]]
2025-03-14T04:07:23.4926184Z + SHM_OPTS=--shm-size=1g
2025-03-14T04:07:23.4926488Z + JENKINS_USER='--user jenkins'
2025-03-14T04:07:23.4926800Z + DOCKER_SHELL_CMD=
2025-03-14T04:07:23.4933133Z +++ nproc --ignore=2
2025-03-14T04:07:23.4946230Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -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 REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=14 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=29g --memory-swap=32g --env-file=/tmp/github_env_13849515290 --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-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:07:38.4747524Z + container_name=a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T04:07:38.4749677Z + grep download.pytorch.org /etc/hosts
2025-03-14T04:07:38.4751485Z + docker exec -i a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad sudo bash -c '/bin/cat >> /etc/hosts'
2025-03-14T04:07:38.6562535Z + echo DOCKER_CONTAINER_ID=a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T04:07:38.6563212Z + [[ linux-focal-py3.13-clang10 == *\s\3\9\0\x* ]]
2025-03-14T04:07:38.6565833Z ++ echo dist/torch-2.8.0a0+gitaed0b7a-cp313-cp313-linux_x86_64.whl
2025-03-14T04:07:38.6568068Z + docker exec -t a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad sh -c 'python3 -m pip install dist/torch-2.8.0a0+gitaed0b7a-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh'
2025-03-14T04:07:39.0768954Z Processing ./dist/torch-2.8.0a0+gitaed0b7a-cp313-cp313-linux_x86_64.whl (from torch==2.8.0a0+gitaed0b7a)
2025-03-14T04:07:39.5037579Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.16.1)
2025-03-14T04:07:39.5040504Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (4.12.2)
2025-03-14T04:07:39.5051837Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (75.8.0)
2025-03-14T04:07:39.5055295Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (1.13.3)
2025-03-14T04:07:39.5057982Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (2.8.8)
2025-03-14T04:07:39.5060572Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.1.6)
2025-03-14T04:07:39.5063190Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (2024.10.0)
2025-03-14T04:07:39.5080910Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.3.0)
2025-03-14T04:07:39.5094312Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (2.1.2)
2025-03-14T04:07:39.5108028Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (1.3.0)
2025-03-14T04:07:39.5212158Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.0.2)
2025-03-14T04:07:39.7183685Z Installing collected packages: torch
2025-03-14T04:07:48.3979143Z Successfully installed torch-2.8.0a0+gitaed0b7a
2025-03-14T04:07:48.4970654Z + export TERM=vt100
2025-03-14T04:07:48.4970991Z + TERM=vt100
2025-03-14T04:07:48.4972414Z ++ dirname .ci/pytorch/test.sh
2025-03-14T04:07:48.4979421Z + source .ci/pytorch/common.sh
2025-03-14T04:07:48.4982695Z +++ dirname .ci/pytorch/common.sh
2025-03-14T04:07:48.4988458Z ++ source .ci/pytorch/common_utils.sh
2025-03-14T04:07:48.4989842Z +++ declare -f -t trap_add
2025-03-14T04:07:48.5001272Z ++ set -ex -o pipefail
2025-03-14T04:07:48.5001618Z ++ [[ linux-focal-py3.13-clang10 == *rocm* ]]
2025-03-14T04:07:48.5001993Z ++ BUILD_TEST_LIBTORCH=0
2025-03-14T04:07:48.5002341Z + [[ linux-focal-py3.13-clang10 != *rocm* ]]
2025-03-14T04:07:48.5002722Z + [[ linux-focal-py3.13-clang10 != *s390x* ]]
2025-03-14T04:07:48.5003095Z + [[ -d /var/lib/jenkins/workspace ]]
2025-03-14T04:07:48.5003452Z ++ stat -c %u /var/lib/jenkins/workspace
2025-03-14T04:07:48.5010725Z + WORKSPACE_ORIGINAL_OWNER_ID=1000
2025-03-14T04:07:48.5011979Z + trap_add cleanup_workspace EXIT
2025-03-14T04:07:48.5012334Z + trap_add_cmd=cleanup_workspace
2025-03-14T04:07:48.5012645Z + shift
2025-03-14T04:07:48.5012895Z + for trap_add_name in "$@"
2025-03-14T04:07:48.5017855Z +++ trap -p EXIT
2025-03-14T04:07:48.5020721Z ++ eval 'extract_trap_cmd '
2025-03-14T04:07:48.5021082Z +++ extract_trap_cmd
2025-03-14T04:07:48.5021364Z +++ printf '%s\n' ''
2025-03-14T04:07:48.5021660Z ++ printf '%s\n' cleanup_workspace
2025-03-14T04:07:48.5022631Z + trap -- '
2025-03-14T04:07:48.5022931Z cleanup_workspace' EXIT
2025-03-14T04:07:48.5023286Z + sudo chown -R jenkins /var/lib/jenkins/workspace
2025-03-14T04:07:48.9975482Z + git config --global --add safe.directory /var/lib/jenkins/workspace
2025-03-14T04:07:48.9991065Z + echo 'Environment variables:'
2025-03-14T04:07:48.9991406Z Environment variables:
2025-03-14T04:07:48.9991682Z + env
2025-03-14T04:07:48.9998720Z INSTALLED_DB=yes
2025-03-14T04:07:48.9999408Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:07:49.0000024Z CONTINUE_THROUGH_ERROR=False
2025-03-14T04:07:49.0000486Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10
2025-03-14T04:07:49.0000950Z HOSTNAME=a62caa0dfbc6
2025-03-14T04:07:49.0001576Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0002249Z GITHUB_ACTION=__self
2025-03-14T04:07:49.0002547Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-14T04:07:49.0002866Z GITHUB_RUN_NUMBER=299021
2025-03-14T04:07:49.0003158Z TEST_CONFIG=default
2025-03-14T04:07:49.0003447Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-14T04:07:49.0003811Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-14T04:07:49.0004167Z IS_A100_RUNNER=0
2025-03-14T04:07:49.0004708Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-14T04:07:49.0005220Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-03-14T04:07:49.0005587Z GITHUB_REF_TYPE=branch
2025-03-14T04:07:49.0005880Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-14T04:07:49.0006236Z BASE_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0006601Z XLA_CUDA=
2025-03-14T04:07:49.0006965Z HUGGING_FACE_HUB_TOKEN=***
2025-03-14T04:07:49.0007452Z ***
2025-03-14T04:07:49.0007703Z GITHUB_REPOSITORY_ID=65600975
2025-03-14T04:07:49.0008015Z GITHUB_ACTIONS=true
2025-03-14T04:07:49.0008321Z SHA1=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0008763Z GITHUB_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0009307Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main
2025-03-14T04:07:49.0009808Z UCC_HOME=/usr
2025-03-14T04:07:49.0010070Z VERBOSE_TEST_LOGS=False
2025-03-14T04:07:49.0010364Z GITHUB_REF=refs/heads/main
2025-03-14T04:07:49.0010899Z SHARD_NUMBER=1
2025-03-14T04:07:49.0011169Z GITHUB_REF_PROTECTED=true
2025-03-14T04:07:49.0011467Z HOME=/var/lib/jenkins
2025-03-14T04:07:49.0011778Z GITHUB_API_URL=https://api.github.com
2025-03-14T04:07:49.0012129Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-14T04:07:49.0012457Z UCX_COMMIT=
2025-03-14T04:07:49.0012708Z NUM_TEST_SHARDS=5
2025-03-14T04:07:49.0012967Z UCX_HOME=/usr
2025-03-14T04:07:49.0013569Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0014404Z JOB_NAME=linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:49.0015204Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0016051Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-14T04:07:49.0016585Z GITHUB_EVENT_NAME=push
2025-03-14T04:07:49.0016870Z DASHBOARD_TAG=
2025-03-14T04:07:49.0017128Z GITHUB_RUN_ID=13849515290
2025-03-14T04:07:49.0017799Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0018528Z GITHUB_ACTOR=pytorchmergebot
2025-03-14T04:07:49.0018939Z PR_NUMBER=
2025-03-14T04:07:49.0019185Z DESIRED_CUDA=
2025-03-14T04:07:49.0019442Z GITHUB_RUN_ATTEMPT=1
2025-03-14T04:07:49.0019730Z ANACONDA_PYTHON_VERSION=3.13
2025-03-14T04:07:49.0020099Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-14T04:07:49.0020468Z TERM=vt100
2025-03-14T04:07:49.0020715Z INSTALLED_VISION=yes
2025-03-14T04:07:49.0020985Z BRANCH=main
2025-03-14T04:07:49.0021239Z SCCACHE_REGION=us-east-1
2025-03-14T04:07:49.0021545Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-14T04:07:49.0021861Z CUDA_PATH=/usr/local/cuda
2025-03-14T04:07:49.0022413Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-14T04:07:49.0023037Z GITHUB_SERVER_URL=https://github.com
2025-03-14T04:07:49.0023366Z UCC_COMMIT=
2025-03-14T04:07:49.0023611Z REENABLED_ISSUES=
2025-03-14T04:07:49.0023866Z DOCS=
2025-03-14T04:07:49.0024091Z SHLVL=1
2025-03-14T04:07:49.0024319Z MAX_JOBS=14
2025-03-14T04:07:49.0024566Z GITHUB_ACTOR_ID=97764156
2025-03-14T04:07:49.0024948Z GITHUB_WORKFLOW_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0025346Z GITHUB_REF_NAME=main
2025-03-14T04:07:49.0025760Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:07:49.0026217Z GITHUB_JOB=test
2025-03-14T04:07:49.0026480Z NO_TEST_TIMEOUT=False
2025-03-14T04:07:49.0026758Z TD_DISTRIBUTED=False
2025-03-14T04:07:49.0027054Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-14T04:07:49.0027385Z GITHUB_RETENTION_DAYS=90
2025-03-14T04:07:49.0027683Z OPENSSL_DIR=/opt/openssl
2025-03-14T04:07:49.0027981Z GITHUB_ACTION_REPOSITORY=
2025-03-14T04:07:49.0028793Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:07:49.0029640Z GITHUB_BASE_REF=
2025-03-14T04:07:49.0029902Z INSTALLED_ACL=
2025-03-14T04:07:49.0030286Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T04:07:49.0030731Z CI=true
2025-03-14T04:07:49.0030993Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-14T04:07:49.0031301Z JOB_ID=38754598997
2025-03-14T04:07:49.0031552Z INSTALLED_PROTOBUF=yes
2025-03-14T04:07:49.0031833Z GITHUB_HEAD_REF=
2025-03-14T04:07:49.0032093Z GITHUB_ACTION_REF=
2025-03-14T04:07:49.0032415Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-14T04:07:49.0032795Z TEST_SHOWLOCALS=False
2025-03-14T04:07:49.0033072Z GITHUB_WORKFLOW=pull
2025-03-14T04:07:49.0033361Z DEBIAN_FRONTEND=noninteractive
2025-03-14T04:07:49.0034024Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0034700Z NO_TD=False
2025-03-14T04:07:49.0035049Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-14T04:07:49.0035354Z _=/usr/bin/env
2025-03-14T04:07:49.0035703Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2025-03-14T04:07:49.0143905Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch
2025-03-14T04:07:49.0144662Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T04:07:49.0145291Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib
2025-03-14T04:07:49.0145972Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test
2025-03-14T04:07:49.0146524Z + BUILD_DIR=build
2025-03-14T04:07:49.0146826Z + BUILD_RENAMED_DIR=build_renamed
2025-03-14T04:07:49.0147211Z + BUILD_BIN_DIR=build/bin
2025-03-14T04:07:49.0147496Z + SHARD_NUMBER=1
2025-03-14T04:07:49.0147742Z + NUM_TEST_SHARDS=5
2025-03-14T04:07:49.0148036Z + export TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:07:49.0148381Z + TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:07:49.0148865Z + export VALGRIND=ON
2025-03-14T04:07:49.0149166Z + VALGRIND=ON
2025-03-14T04:07:49.0149463Z + [[ linux-focal-py3.13-clang10 == *clang9* ]]
2025-03-14T04:07:49.0149856Z + [[ linux-focal-py3.13-clang10 == *xpu* ]]
2025-03-14T04:07:49.0150241Z + [[ linux-focal-py3.13-clang10 == *s390x* ]]
2025-03-14T04:07:49.0150641Z + [[ 0 == \1 ]]
2025-03-14T04:07:49.0151133Z + [[ False == \1 ]]
2025-03-14T04:07:49.0151445Z + [[ linux-focal-py3.13-clang10 != *bazel* ]]
2025-03-14T04:07:49.0151830Z ++ realpath build/custom_test_artifacts
2025-03-14T04:07:49.0157655Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2025-03-14T04:07:49.0158250Z + [[ -n '' ]]
2025-03-14T04:07:49.0158517Z + echo 'Environment variables'
2025-03-14T04:07:49.0158835Z Environment variables
2025-03-14T04:07:49.0159175Z + env
2025-03-14T04:07:49.0164694Z INSTALLED_DB=yes
2025-03-14T04:07:49.0165165Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:07:49.0165698Z CONTINUE_THROUGH_ERROR=False
2025-03-14T04:07:49.0166071Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10
2025-03-14T04:07:49.0166481Z HOSTNAME=a62caa0dfbc6
2025-03-14T04:07:49.0167133Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0167822Z GITHUB_ACTION=__self
2025-03-14T04:07:49.0168162Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-14T04:07:49.0168495Z GITHUB_RUN_NUMBER=299021
2025-03-14T04:07:49.0168780Z TEST_CONFIG=default
2025-03-14T04:07:49.0169065Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-14T04:07:49.0169420Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-14T04:07:49.0169757Z IS_A100_RUNNER=0
2025-03-14T04:07:49.0170318Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-14T04:07:49.0170750Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-03-14T04:07:49.0171111Z GITHUB_REF_TYPE=branch
2025-03-14T04:07:49.0171404Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-14T04:07:49.0171807Z BASE_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0172234Z XLA_CUDA=
2025-03-14T04:07:49.0172630Z HUGGING_FACE_HUB_TOKEN=***
2025-03-14T04:07:49.0173169Z ***
2025-03-14T04:07:49.0173421Z GITHUB_REPOSITORY_ID=65600975
2025-03-14T04:07:49.0173789Z GITHUB_ACTIONS=true
2025-03-14T04:07:49.0174091Z SHA1=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0174547Z GITHUB_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0175075Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main
2025-03-14T04:07:49.0175572Z UCC_HOME=/usr
2025-03-14T04:07:49.0175836Z TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:07:49.0176150Z VERBOSE_TEST_LOGS=False
2025-03-14T04:07:49.0176441Z GITHUB_REF=refs/heads/main
2025-03-14T04:07:49.0176730Z SHARD_NUMBER=1
2025-03-14T04:07:49.0176999Z GITHUB_REF_PROTECTED=true
2025-03-14T04:07:49.0177288Z HOME=/var/lib/jenkins
2025-03-14T04:07:49.0177593Z GITHUB_API_URL=https://api.github.com
2025-03-14T04:07:49.0177953Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-14T04:07:49.0178271Z UCX_COMMIT=
2025-03-14T04:07:49.0178694Z NUM_TEST_SHARDS=5
2025-03-14T04:07:49.0178955Z UCX_HOME=/usr
2025-03-14T04:07:49.0179563Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0180392Z JOB_NAME=linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T04:07:49.0181201Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0182041Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-14T04:07:49.0182570Z GITHUB_EVENT_NAME=push
2025-03-14T04:07:49.0182831Z DASHBOARD_TAG=
2025-03-14T04:07:49.0183092Z GITHUB_RUN_ID=13849515290
2025-03-14T04:07:49.0183761Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0184484Z GITHUB_ACTOR=pytorchmergebot
2025-03-14T04:07:49.0184785Z PR_NUMBER=
2025-03-14T04:07:49.0185025Z DESIRED_CUDA=
2025-03-14T04:07:49.0185276Z GITHUB_RUN_ATTEMPT=1
2025-03-14T04:07:49.0185543Z VALGRIND=ON
2025-03-14T04:07:49.0185797Z ANACONDA_PYTHON_VERSION=3.13
2025-03-14T04:07:49.0186163Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-14T04:07:49.0186538Z TERM=vt100
2025-03-14T04:07:49.0186779Z INSTALLED_VISION=yes
2025-03-14T04:07:49.0187121Z BRANCH=main
2025-03-14T04:07:49.0187372Z SCCACHE_REGION=us-east-1
2025-03-14T04:07:49.0187676Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-14T04:07:49.0187971Z CUDA_PATH=/usr/local/cuda
2025-03-14T04:07:49.0188520Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-14T04:07:49.0189133Z GITHUB_SERVER_URL=https://github.com
2025-03-14T04:07:49.0189456Z UCC_COMMIT=
2025-03-14T04:07:49.0189695Z REENABLED_ISSUES=
2025-03-14T04:07:49.0189942Z DOCS=
2025-03-14T04:07:49.0190163Z SHLVL=1
2025-03-14T04:07:49.0190384Z MAX_JOBS=14
2025-03-14T04:07:49.0190628Z GITHUB_ACTOR_ID=97764156
2025-03-14T04:07:49.0191007Z GITHUB_WORKFLOW_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:07:49.0191417Z GITHUB_REF_NAME=main
2025-03-14T04:07:49.0191823Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:07:49.0192285Z GITHUB_JOB=test
2025-03-14T04:07:49.0192547Z NO_TEST_TIMEOUT=False
2025-03-14T04:07:49.0192832Z TD_DISTRIBUTED=False
2025-03-14T04:07:49.0193114Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-14T04:07:49.0193444Z GITHUB_RETENTION_DAYS=90
2025-03-14T04:07:49.0193734Z OPENSSL_DIR=/opt/openssl
2025-03-14T04:07:49.0194027Z GITHUB_ACTION_REPOSITORY=
2025-03-14T04:07:49.0194834Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:07:49.0195663Z GITHUB_BASE_REF=
2025-03-14T04:07:49.0195921Z INSTALLED_ACL=
2025-03-14T04:07:49.0196301Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T04:07:49.0196741Z CI=true
2025-03-14T04:07:49.0196997Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-14T04:07:49.0197307Z JOB_ID=38754598997
2025-03-14T04:07:49.0197577Z INSTALLED_PROTOBUF=yes
2025-03-14T04:07:49.0197857Z GITHUB_HEAD_REF=
2025-03-14T04:07:49.0198124Z GITHUB_ACTION_REF=
2025-03-14T04:07:49.0198445Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-14T04:07:49.0198833Z TEST_SHOWLOCALS=False
2025-03-14T04:07:49.0199192Z GITHUB_WORKFLOW=pull
2025-03-14T04:07:49.0199494Z DEBIAN_FRONTEND=noninteractive
2025-03-14T04:07:49.0200158Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_366d53d3-dafd-4ff0-993b-8eb0d3c5204d
2025-03-14T04:07:49.0200832Z NO_TD=False
2025-03-14T04:07:49.0201095Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-14T04:07:49.0201403Z _=/usr/bin/env
2025-03-14T04:07:49.0201662Z + echo 'Testing pytorch'
2025-03-14T04:07:49.0201950Z Testing pytorch
2025-03-14T04:07:49.0202219Z + export LANG=C.UTF-8
2025-03-14T04:07:49.0202492Z + LANG=C.UTF-8
2025-03-14T04:07:49.0202743Z + PR_NUMBER=
2025-03-14T04:07:49.0203124Z + [[ default == \d\e\f\a\u\l\t ]]
2025-03-14T04:07:49.0203453Z + export CUDA_VISIBLE_DEVICES=0
2025-03-14T04:07:49.0203766Z + CUDA_VISIBLE_DEVICES=0
2025-03-14T04:07:49.0204067Z + export HIP_VISIBLE_DEVICES=0
2025-03-14T04:07:49.0204359Z + HIP_VISIBLE_DEVICES=0
2025-03-14T04:07:49.0204681Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]]
2025-03-14T04:07:49.0205021Z + [[ default == \s\l\o\w ]]
2025-03-14T04:07:49.0205377Z + [[ linux-focal-py3.13-clang10 == *slow-gradcheck* ]]
2025-03-14T04:07:49.0205791Z + [[ linux-focal-py3.13-clang10 == *cuda* ]]
2025-03-14T04:07:49.0206165Z + [[ linux-focal-py3.13-clang10 == *rocm* ]]
2025-03-14T04:07:49.0206535Z + [[ linux-focal-py3.13-clang10 == *xpu* ]]
2025-03-14T04:07:49.0206891Z + [[ default == *crossref* ]]
2025-03-14T04:07:49.0207221Z + [[ linux-focal-py3.13-clang10 == *rocm* ]]
2025-03-14T04:07:49.0207586Z + [[ linux-focal-py3.13-clang10 == *xpu* ]]
2025-03-14T04:07:49.0207964Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]]
2025-03-14T04:07:49.0208348Z + pip_install --user ninja==1.10.2
2025-03-14T04:07:49.0208775Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:07:49.0209299Z + python3 -m pip install --progress-bar off --user ninja==1.10.2
2025-03-14T04:07:49.5165081Z Collecting ninja==1.10.2
2025-03-14T04:07:49.5536739Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB)
2025-03-14T04:07:49.5814461Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2025-03-14T04:07:49.7610327Z Installing collected packages: ninja
2025-03-14T04:07:49.7686236Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2025-03-14T04:07:49.7687239Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2025-03-14T04:07:49.7788822Z Successfully installed ninja-1.10.2
2025-03-14T04:07:49.8732521Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:07:49.8734214Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:07:49.8735513Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]]
2025-03-14T04:07:49.8735879Z + install_tlparse
2025-03-14T04:07:49.8736175Z + pip_install --user tlparse==0.3.30
2025-03-14T04:07:49.8736607Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:07:49.8737137Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30
2025-03-14T04:07:50.3498919Z Collecting tlparse==0.3.30
2025-03-14T04:07:50.3844607Z   Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB)
2025-03-14T04:07:50.4260651Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)
2025-03-14T04:07:50.6862426Z Installing collected packages: tlparse
2025-03-14T04:07:50.7919094Z Successfully installed tlparse-0.3.30
2025-03-14T04:07:50.8888027Z ++ python -m site --user-base
2025-03-14T04:07:50.9035058Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:07:50.9036202Z + [[ linux-focal-py3.13-clang10 == *asan* ]]
2025-03-14T04:07:50.9037003Z + [[ linux-focal-py3.13-clang10 == *-debug* ]]
2025-03-14T04:07:50.9037459Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]]
2025-03-14T04:07:50.9038024Z + echo 'We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass'
2025-03-14T04:07:50.9038707Z We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass
2025-03-14T04:07:50.9040211Z + cd test
2025-03-14T04:07:50.9040663Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)'
2025-03-14T04:07:52.3249170Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2025-03-14T04:07:52.3249617Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2025-03-14T04:07:52.3255011Z + DYNAMO_BENCHMARK_FLAGS=()
2025-03-14T04:07:52.3256274Z + [[ default == *pr_time_benchmarks* ]]
2025-03-14T04:07:52.3256705Z + [[ default == *dynamo_eager* ]]
2025-03-14T04:07:52.3257029Z + [[ default == *aot_eager* ]]
2025-03-14T04:07:52.3257343Z + [[ default == *aot_inductor* ]]
2025-03-14T04:07:52.3257691Z + [[ default == *max_autotune_inductor* ]]
2025-03-14T04:07:52.3258038Z + [[ default == *inductor* ]]
2025-03-14T04:07:52.3258340Z + [[ default == *dynamic* ]]
2025-03-14T04:07:52.3258637Z + [[ default == *cpu* ]]
2025-03-14T04:07:52.3258959Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
2025-03-14T04:07:52.3289864Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]]
2025-03-14T04:07:52.3290272Z + [[ linux-focal-py3.13-clang10 == *-bazel-* ]]
2025-03-14T04:07:52.3292833Z + cd test
2025-03-14T04:07:52.3293347Z + python -c 'import torch; print(torch.__config__.show())'
2025-03-14T04:07:53.4879800Z PyTorch built with:
2025-03-14T04:07:53.4880138Z   - GCC 4.2
2025-03-14T04:07:53.4880407Z   - C++ Version: 201703
2025-03-14T04:07:53.4880697Z   - clang 10.0.0
2025-03-14T04:07:53.4881970Z   - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-14T04:07:53.4882771Z   - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-03-14T04:07:53.4883257Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-14T04:07:53.4883635Z   - LAPACK is enabled (usually provided by MKL)
2025-03-14T04:07:53.4884006Z   - NNPACK is enabled
2025-03-14T04:07:53.4884307Z   - CPU capability usage: AVX512
2025-03-14T04:07:53.4889535Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Qunused-arguments -fcolor-diagnostics -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 
2025-03-14T04:07:53.4894910Z 
2025-03-14T04:07:53.7480598Z + cd test
2025-03-14T04:07:53.7481037Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2025-03-14T04:07:54.9033921Z ATen/Parallel:
2025-03-14T04:07:54.9034302Z 	at::get_num_threads() : 8
2025-03-14T04:07:54.9034661Z 	at::get_num_interop_threads() : 8
2025-03-14T04:07:54.9035007Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-14T04:07:54.9035336Z 	omp_get_max_threads() : 8
2025-03-14T04:07:54.9035933Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-14T04:07:54.9036570Z 	mkl_get_max_threads() : 8
2025-03-14T04:07:54.9036990Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-03-14T04:07:54.9037471Z std::thread::hardware_concurrency() : 16
2025-03-14T04:07:54.9037820Z Environment variables:
2025-03-14T04:07:54.9038110Z 	OMP_NUM_THREADS : [not set]
2025-03-14T04:07:54.9038411Z 	MKL_NUM_THREADS : [not set]
2025-03-14T04:07:54.9038982Z ATen parallel backend: OpenMP
2025-03-14T04:07:54.9039254Z 
2025-03-14T04:07:55.1830171Z + [[ default == *numpy_2* ]]
2025-03-14T04:07:55.1830638Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]]
2025-03-14T04:07:55.1831067Z + [[ default == *backward* ]]
2025-03-14T04:07:55.1831361Z + [[ default == *xla* ]]
2025-03-14T04:07:55.1831700Z + [[ default == *executorch* ]]
2025-03-14T04:07:55.1832020Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]]
2025-03-14T04:07:55.1832399Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]]
2025-03-14T04:07:55.1832777Z + [[ default == distributed ]]
2025-03-14T04:07:55.1833107Z + [[ default == *inductor_distributed* ]]
2025-03-14T04:07:55.1833468Z + [[ default == *inductor-halide* ]]
2025-03-14T04:07:55.1833818Z + [[ default == *inductor-triton-cpu* ]]
2025-03-14T04:07:55.1834199Z + [[ default == *inductor-micro-benchmark* ]]
2025-03-14T04:07:55.1834561Z + [[ default == *huggingface* ]]
2025-03-14T04:07:55.1834871Z + [[ default == *timm* ]]
2025-03-14T04:07:55.1835176Z + [[ default == cachebench ]]
2025-03-14T04:07:55.1835493Z + [[ default == verify_cachebench ]]
2025-03-14T04:07:55.1835826Z + [[ default == *torchbench* ]]
2025-03-14T04:07:55.1836152Z + [[ default == *inductor_cpp_wrapper* ]]
2025-03-14T04:07:55.1836500Z + [[ default == *inductor* ]]
2025-03-14T04:07:55.1837070Z + [[ default == *dynamo_wrapped* ]]
2025-03-14T04:07:55.1837429Z + [[ linux-focal-py3.13-clang10 == *rocm* ]]
2025-03-14T04:07:55.1837755Z + [[ 1 == 1 ]]
2025-03-14T04:07:55.1838011Z + [[ 5 -gt 1 ]]
2025-03-14T04:07:55.1838305Z + test_lazy_tensor_meta_reference_disabled
2025-03-14T04:07:55.1838732Z + export TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1
2025-03-14T04:07:55.1839271Z + TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1
2025-03-14T04:07:55.1839743Z + echo 'Testing lazy tensor operations without meta reference'
2025-03-14T04:07:55.1840227Z Testing lazy tensor operations without meta reference
2025-03-14T04:07:55.1840737Z + python test/run_test.py --include lazy/test_ts_opinfo.py --verbose
2025-03-14T04:07:55.2873047Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-14T04:07:55.2873966Z   import pkg_resources
2025-03-14T04:07:59.0790936Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-14T04:07:59.1511512Z Ignoring disabled issues:  ['']
2025-03-14T04:07:59.1686454Z Found test times from artifacts
2025-03-14T04:07:59.2392561Z Found test times from artifacts
2025-03-14T04:07:59.2415516Z Running all tests
2025-03-14T04:07:59.2418797Z Running parallel tests on 3 processes
2025-03-14T04:07:59.2419537Z Name: tests to run (est. time: 0.42min)
2025-03-14T04:07:59.2419940Z   Serial tests (0):
2025-03-14T04:07:59.2420219Z   Parallel tests (1):
2025-03-14T04:07:59.2420512Z     lazy/test_ts_opinfo 1/1
2025-03-14T04:07:59.2420829Z Name: excluded (est. time: 0.0min)
2025-03-14T04:07:59.2421178Z   Serial tests (0):
2025-03-14T04:07:59.2421430Z   Parallel tests (0):
2025-03-14T04:07:59.2474448Z Running lazy/test_ts_opinfo 1/1 ... [2025-03-14 04:07:59.247144]
2025-03-14T04:07:59.2474919Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:07:59.2478326Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:07:59.247446]
2025-03-14T04:08:03.7677268Z 
2025-03-14T04:08:03.7678259Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_ac31da7f7e471f98_.log
2025-03-14T04:08:03.7679109Z Running 0 items in this shard:
2025-03-14T04:08:03.7679318Z 
2025-03-14T04:08:03.7683069Z Running lazy/test_ts_opinfo 1/1 ... [2025-03-14 04:08:03.768146]
2025-03-14T04:08:03.7683509Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:08:03.7687038Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:08:03.768509]
2025-03-14T04:08:19.4531587Z 
2025-03-14T04:08:19.4532378Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_eb6063a576bf3a00_.log
2025-03-14T04:08:19.4646872Z Running 292 items in this shard: test/lazy/test_ts_opinfo.py::TestLazyTensor::testConvolutionBackward, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_tensor_ctr, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_view_mark_step_preserved, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcdiv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bernoulli_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_pow_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_adaptiveavgpool3d_dynamic, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_nonzero_dynamic
2025-03-14T04:08:19.4757876Z 
2025-03-14T04:08:20.2544813Z Running test batch 'tests to run' cost 21.01 seconds
2025-03-14T04:08:21.0264829Z 
2025-03-14T04:08:21.0265250Z real	0m25.843s
2025-03-14T04:08:21.0265562Z user	1m28.498s
2025-03-14T04:08:21.0265813Z sys	0m4.241s
2025-03-14T04:08:21.0266480Z + export -n TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE
2025-03-14T04:08:21.0266905Z + test_without_numpy
2025-03-14T04:08:21.0269087Z ++ dirname .ci/pytorch/test.sh
2025-03-14T04:08:21.0278452Z + pushd .ci/pytorch
2025-03-14T04:08:21.0279240Z ~/workspace/.ci/pytorch ~/workspace
2025-03-14T04:08:21.0280187Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');from unittest import TestCase;import torch;x=torch.randn(3,3);TestCase().assertRaises(RuntimeError, lambda: x.numpy())'
2025-03-14T04:08:21.7460737Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:276: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:81.)
2025-03-14T04:08:21.7462080Z   cpu = _conversion_method_template(device=torch.device("cpu"))
2025-03-14T04:08:22.3654731Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch;print(torch.tensor([torch.tensor(0.), torch.tensor(1.)]))'
2025-03-14T04:08:23.0843027Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:276: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:81.)
2025-03-14T04:08:23.0844616Z   cpu = _conversion_method_template(device=torch.device("cpu"))
2025-03-14T04:08:23.4613029Z tensor([0., 1.])
2025-03-14T04:08:23.7065154Z + [[ default == *dynamo_wrapped* ]]
2025-03-14T04:08:23.7065558Z + popd
2025-03-14T04:08:23.7065794Z ~/workspace
2025-03-14T04:08:23.7066043Z + install_torchvision
2025-03-14T04:08:23.7066321Z + local orig_preload
2025-03-14T04:08:23.7066593Z + local commit
2025-03-14T04:08:23.7068835Z ++ get_pinned_commit vision
2025-03-14T04:08:23.7069169Z ++ cat .github/ci_commit_pins/vision.txt
2025-03-14T04:08:23.7081864Z + commit=d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:08:23.7082242Z + orig_preload=
2025-03-14T04:08:23.7082486Z + '[' -n '' ']'
2025-03-14T04:08:23.7083122Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:08:23.7083872Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:08:23.7084732Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:08:24.0829086Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:08:24.0833076Z   Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-gzwf6ih7
2025-03-14T04:08:24.0852695Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-gzwf6ih7
2025-03-14T04:08:25.5164446Z   Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f'
2025-03-14T04:08:25.5185961Z   Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:08:26.9140727Z   Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:08:27.2482560Z   Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:08:29.2836910Z   Preparing metadata (setup.py) ... [?25l- \ done
2025-03-14T04:08:29.2868794Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.1.2)
2025-03-14T04:08:29.2872165Z Requirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.8.0a0+gitaed0b7a)
2025-03-14T04:08:29.2876588Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0)
2025-03-14T04:08:29.2940971Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1)
2025-03-14T04:08:29.2944599Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2)
2025-03-14T04:08:29.2955785Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (75.8.0)
2025-03-14T04:08:29.2959521Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.3)
2025-03-14T04:08:29.2962418Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8)
2025-03-14T04:08:29.2965311Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.6)
2025-03-14T04:08:29.2968216Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0)
2025-03-14T04:08:29.2982478Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0)
2025-03-14T04:08:29.3084875Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2)
2025-03-14T04:08:29.3186793Z Building wheels for collected packages: torchvision
2025-03-14T04:09:05.4294228Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
2025-03-14T04:09:05.4331218Z [?25h  Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp313-cp313-linux_x86_64.whl size=1162259 sha256=c292eb2808b3a489a36e1771c2f8ee348945d2dba5837d9ad435ddc88e5f3d2e
2025-03-14T04:09:05.4332658Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/81/60/73/f2acb628a45eebe28dd9ff5468e774a0d5e194728570f8ff6f
2025-03-14T04:09:05.4367103Z Successfully built torchvision
2025-03-14T04:09:05.5749961Z Installing collected packages: torchvision
2025-03-14T04:09:06.0305604Z Successfully installed torchvision-0.19.0a0+d23a6e1
2025-03-14T04:09:06.1512388Z + '[' -n '' ']'
2025-03-14T04:09:06.1512713Z + test_python_shard 1
2025-03-14T04:09:06.1513005Z + [[ -z 5 ]]
2025-03-14T04:09:06.1513667Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 1 5 --verbose --upload-artifacts-while-running
2025-03-14T04:09:06.2534651Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-14T04:09:06.2535575Z   import pkg_resources
2025-03-14T04:09:10.0157409Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-14T04:09:10.0328662Z Found test times from artifacts
2025-03-14T04:09:10.1036558Z Found test times from artifacts
2025-03-14T04:09:10.1061242Z Running all tests
2025-03-14T04:09:10.1407800Z Running parallel tests on 3 processes
2025-03-14T04:09:10.1415715Z Name: tests to run (est. time: 104.18min)
2025-03-14T04:09:10.1416394Z   Serial tests (46):
2025-03-14T04:09:10.1416735Z     test_ci_sanity_check_fail 1/1
2025-03-14T04:09:10.1417069Z     inductor/test_flex_attention 1/1
2025-03-14T04:09:10.1417455Z     test_reductions 1/1
2025-03-14T04:09:10.1417912Z     test_overrides 1/1
2025-03-14T04:09:10.1418454Z     test_cpp_extensions_aot_ninja 1/1
2025-03-14T04:09:10.1418835Z     inductor/test_distributed_patterns 1/1
2025-03-14T04:09:10.1419191Z     test_cpp_api_parity 1/1
2025-03-14T04:09:10.1419507Z     inductor/test_benchmark_fusion 1/1
2025-03-14T04:09:10.1419848Z     test_extension_utils 1/1
2025-03-14T04:09:10.1420371Z     inductor/test_cutlass_backend 1/1
2025-03-14T04:09:10.1420703Z     test_show_pickle 1/1
2025-03-14T04:09:10.1420994Z     test_transformers_privateuse1 1/1
2025-03-14T04:09:10.1421386Z     test_cpp_extensions_open_device_registration 1/1
2025-03-14T04:09:10.1421778Z     test_namedtuple_return_api 1/1
2025-03-14T04:09:10.1422114Z     test_tensorexpr 1/1
2025-03-14T04:09:10.1422397Z     test_utils 1/1
2025-03-14T04:09:10.1422733Z     test_multiprocessing 1/1
2025-03-14T04:09:10.1423034Z     test_torch 1/1
2025-03-14T04:09:10.1423296Z     test_autocast 1/1
2025-03-14T04:09:10.1423566Z     test_autograd_fallback 1/1
2025-03-14T04:09:10.1423883Z     test_python_dispatch 1/1
2025-03-14T04:09:10.1424187Z     test_jit_disabled 1/1
2025-03-14T04:09:10.1424478Z     test_fake_tensor 1/1
2025-03-14T04:09:10.1424759Z     test_fx 1/1
2025-03-14T04:09:10.1425036Z     test_cpp_extensions_mtia_backend 1/1
2025-03-14T04:09:10.1425393Z     inductor/test_max_autotune 1/1
2025-03-14T04:09:10.1425748Z     test_cpp_extensions_stream_and_event 1/1
2025-03-14T04:09:10.1426108Z     test_cpp_extensions_jit 1/1
2025-03-14T04:09:10.1426418Z     test_native_mha 1/1
2025-03-14T04:09:10.1426707Z     test_tensor_creation_ops 1/1
2025-03-14T04:09:10.1427024Z     test_sort_and_select 1/1
2025-03-14T04:09:10.1427322Z     test_dispatch 1/1
2025-03-14T04:09:10.1427713Z     test_cuda_primary_ctx 1/1
2025-03-14T04:09:10.1428040Z     test_multiprocessing_spawn 1/1
2025-03-14T04:09:10.1428359Z     nn/test_convolution 1/1
2025-03-14T04:09:10.1428662Z     nn/test_pooling 1/1
2025-03-14T04:09:10.1428947Z     test_mobile_optimizer 1/1
2025-03-14T04:09:10.1429242Z     test_nn 1/1
2025-03-14T04:09:10.1429509Z     test_cuda_nvml_based_avail 1/1
2025-03-14T04:09:10.1429827Z     test_cuda_trace 1/1
2025-03-14T04:09:10.1430103Z     test_spectral_ops 1/1
2025-03-14T04:09:10.1430415Z     distributions/test_distributions 1/1
2025-03-14T04:09:10.1430767Z     test_autoload_disable 1/1
2025-03-14T04:09:10.1431061Z     doctests 1/1
2025-03-14T04:09:10.1431331Z     test_autoload_enable 1/1
2025-03-14T04:09:10.1431651Z     test_cpp_extensions_aot_no_ninja 1/1
2025-03-14T04:09:10.1431993Z   Parallel tests (113):
2025-03-14T04:09:10.1432287Z     dynamo/test_cudagraphs 1/1
2025-03-14T04:09:10.1432609Z     inductor/test_cudacodecache 1/1
2025-03-14T04:09:10.1432972Z     inductor/test_torchinductor_strided_blocks 1/1
2025-03-14T04:09:10.1433363Z     inductor/test_triton_cpu_backend 1/1
2025-03-14T04:09:10.1433710Z     export/test_export_legacy 1/1
2025-03-14T04:09:10.1434057Z     inductor/test_aot_inductor_custom_ops 1/1
2025-03-14T04:09:10.1434455Z     inductor/test_torchinductor_dynamic_shapes 4/4
2025-03-14T04:09:10.1434840Z     inductor/test_op_dtype_prop 1/1
2025-03-14T04:09:10.1435186Z     inductor/test_pattern_matcher 1/1
2025-03-14T04:09:10.1435598Z     inductor/test_torchinductor_codegen_dynamic_shapes 2/4
2025-03-14T04:09:10.1436079Z     inductor/test_torchinductor_codegen_dynamic_shapes 3/4
2025-03-14T04:09:10.1436551Z     inductor/test_torchinductor_codegen_dynamic_shapes 4/4
2025-03-14T04:09:10.1436984Z     inductor/test_cpu_select_algorithm 5/16
2025-03-14T04:09:10.1437364Z     inductor/test_cpu_select_algorithm 13/16
2025-03-14T04:09:10.1437745Z     inductor/test_cpu_select_algorithm 15/16
2025-03-14T04:09:10.1438122Z     inductor/test_torchinductor_opinfo 3/42
2025-03-14T04:09:10.1438502Z     inductor/test_torchinductor_opinfo 10/42
2025-03-14T04:09:10.1438884Z     inductor/test_torchinductor_opinfo 13/42
2025-03-14T04:09:10.1439352Z     inductor/test_torchinductor_opinfo 18/42
2025-03-14T04:09:10.1439730Z     inductor/test_torchinductor_opinfo 25/42
2025-03-14T04:09:10.1440112Z     inductor/test_torchinductor_opinfo 28/42
2025-03-14T04:09:10.1440476Z     inductor/test_torchinductor_opinfo 33/42
2025-03-14T04:09:10.1440861Z     inductor/test_torchinductor_opinfo 40/42
2025-03-14T04:09:10.1441229Z     inductor/test_block_analysis 1/1
2025-03-14T04:09:10.1441566Z     dynamo/test_sources 1/1
2025-03-14T04:09:10.1441875Z     inductor/test_fuzzer 1/1
2025-03-14T04:09:10.1442288Z     inductor/test_custom_lowering 1/1
2025-03-14T04:09:10.1442638Z     dynamo/test_recompiles 1/1
2025-03-14T04:09:10.1442955Z     test_namedtensor 1/1
2025-03-14T04:09:10.1443256Z     dynamo/test_optimizers 1/1
2025-03-14T04:09:10.1443574Z     inductor/test_cuda_repro 1/1
2025-03-14T04:09:10.1443904Z     inductor/test_minifier 1/1
2025-03-14T04:09:10.1444213Z     dynamo/test_modes 1/1
2025-03-14T04:09:10.1444551Z     torch_np/numpy_tests/core/test_scalarinherit 1/1
2025-03-14T04:09:10.1444936Z     dynamo/test_decorators 1/1
2025-03-14T04:09:10.1445253Z     test_functionalization 1/1
2025-03-14T04:09:10.1445573Z     inductor/test_triton_kernels 1/1
2025-03-14T04:09:10.1445899Z     nn/test_multihead_attention 1/1
2025-03-14T04:09:10.1446228Z     test_stateless 1/1
2025-03-14T04:09:10.1446504Z     export/test_db 1/1
2025-03-14T04:09:10.1446784Z     inductor/test_perf 1/1
2025-03-14T04:09:10.1447077Z     test_content_store 1/1
2025-03-14T04:09:10.1447378Z     inductor/test_async_compile 1/1
2025-03-14T04:09:10.1447720Z     dynamo/test_autograd_function 1/1
2025-03-14T04:09:10.1448058Z     test_expanded_weights 1/1
2025-03-14T04:09:10.1448394Z     inductor/test_custom_post_grad_passes 1/1
2025-03-14T04:09:10.1448927Z     torch_np/test_basic 1/1
2025-03-14T04:09:10.1449241Z     inductor/test_control_flow 1/1
2025-03-14T04:09:10.1449691Z     inductor/test_ordered_set 1/1
2025-03-14T04:09:10.1450022Z     nn/test_packed_sequence 1/1
2025-03-14T04:09:10.1450342Z     dynamo/test_metrics_context 1/1
2025-03-14T04:09:10.1450670Z     dynamo/test_base_hop 1/1
2025-03-14T04:09:10.1450956Z     test_custom_ops 1/1
2025-03-14T04:09:10.1451239Z     dynamo/test_modules 1/1
2025-03-14T04:09:10.1451555Z     inductor/test_fused_attention 1/1
2025-03-14T04:09:10.1451908Z     inductor/test_layout_optim 1/1
2025-03-14T04:09:10.1452253Z     inductor/test_inductor_freezing 1/1
2025-03-14T04:09:10.1452601Z     inductor/test_mmdecomp 1/1
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2025-03-14T04:09:10.1453252Z     dynamo/test_dicts 1/1
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2025-03-14T04:09:10.1453892Z     test_legacy_vmap 1/1
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2025-03-14T04:09:10.1454748Z     dynamo/test_flat_apply 1/1
2025-03-14T04:09:10.1455062Z     export/test_passes 1/1
2025-03-14T04:09:10.1455363Z     dynamo/test_aot_autograd 1/1
2025-03-14T04:09:10.1455707Z     inductor/test_split_cat_fx_aten_passes 1/1
2025-03-14T04:09:10.1456057Z     export/test_converter 1/1
2025-03-14T04:09:10.1456391Z     inductor/test_efficient_conv_bn_eval 1/1
2025-03-14T04:09:10.1456768Z     dynamo/test_backward_higher_order_ops 1/1
2025-03-14T04:09:10.1457120Z     test_autograd 1/1
2025-03-14T04:09:10.1457401Z     test_functional_optim 1/1
2025-03-14T04:09:10.1457707Z     dynamo/test_resume 1/1
2025-03-14T04:09:10.1458003Z     dynamo/test_export 1/1
2025-03-14T04:09:10.1458305Z     dynamo/test_recompile_ux 1/1
2025-03-14T04:09:10.1458624Z     dynamo/test_unspec 1/1
2025-03-14T04:09:10.1458917Z     nn/test_lazy_modules 1/1
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2025-03-14T04:09:10.1462428Z     torch_np/numpy_tests/core/test_getlimits 1/1
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2025-03-14T04:09:10.1463482Z     test_jit_autocast 1/1
2025-03-14T04:09:10.1463775Z     nn/test_init 1/1
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2025-03-14T04:09:10.1469313Z     torch_np/numpy_tests/core/test_scalar_methods 1/1
2025-03-14T04:09:10.1469684Z     test_sparse 1/1
2025-03-14T04:09:10.1469964Z     functorch/test_aotdispatch 4/5
2025-03-14T04:09:10.1470291Z     test_quantization 3/3
2025-03-14T04:09:10.1470591Z Name: excluded (est. time: 0.0min)
2025-03-14T04:09:10.1470906Z   Serial tests (0):
2025-03-14T04:09:10.1471236Z   Parallel tests (0):
2025-03-14T04:09:10.1492840Z Running test_ci_sanity_check_fail 1/1 ... [2025-03-14 04:09:10.149012]
2025-03-14T04:09:10.1493407Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:09:10.1495980Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:09:10.149325]
2025-03-14T04:09:20.7595470Z Running inductor/test_flex_attention 1/1 ... [2025-03-14 04:09:20.759146]
2025-03-14T04:09:20.7596258Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:09:20.7597678Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:09:20.759466]
2025-03-14T04:09:25.6832763Z 
2025-03-14T04:09:25.6834056Z inductor/test_flex_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_1.1_93d0e6158cb1ac5e_.log
2025-03-14T04:09:25.7180994Z Running 664 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_causal_mask, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod0, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod1, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod2, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod3, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod4, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod5, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod6, test/inductor/test_flex_attention.py::TestFlexAttention::test_GQA_float32_score_mod7, test/inductor/test_flex_attention.py::TestFlexAttention::test_aot_eager_gradcheck_score_mod0, 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test/inductor/test_flex_attention.py::TestLearnableBiases::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32
2025-03-14T04:09:25.7501355Z 
2025-03-14T04:09:25.7501599Z Running test_reductions 1/1 ... [2025-03-14 04:09:25.684510]
2025-03-14T04:09:25.7502046Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:09:25.7503346Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:09:25.684803]
2025-03-14T04:12:48.0242916Z 
2025-03-14T04:12:48.0244240Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_7072b687df27fa6e_.log
2025-03-14T04:12:48.1944273Z Running 4565 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_amin_amax_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, 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2025-03-14T04:12:48.3593514Z 
2025-03-14T04:12:48.3593733Z Running test_overrides 1/1 ... [2025-03-14 04:12:48.030928]
2025-03-14T04:12:48.3594202Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:12:48.3595292Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_overrides.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:12:48.031380]
2025-03-14T04:12:55.3555132Z 
2025-03-14T04:12:55.3556001Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_0dffc0742b28efd6_.log
2025-03-14T04:12:55.4033290Z Running 1467 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__version___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_data___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_device___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_dtype___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_imag___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cuda___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_ipu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_leaf___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_maia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_meta___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mkldnn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mps___get__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copysign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cos, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cos_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cosh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cov, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cross, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_crow_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cuda, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_data_ptr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dense_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_det, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diff, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dim_order, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_div, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_div_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_double, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_element_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_eq, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_eq_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erf_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfinv_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expand, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expand_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expm1_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exponential_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fill_diagonal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fix, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fix_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flip, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float_power_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gather, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_geometric_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ger, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_get_device, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_pinned, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_set_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_shared, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_istft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_item, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kron, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_median, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mode, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mtia, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ndimension, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nelement, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_norm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numpy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_outer, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_permute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pin_memory, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_positive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qscheme, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_random_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_record_stream, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_refine_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_post_accumulate_grad_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_requires_grad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_sparse_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_retain_grad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_roll, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_row_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_set_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_share_memory_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_short, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_smm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_mask, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_and_clear_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sspaddmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_std, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_stft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_offset, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum_to_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_svd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_mkldnn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_sparse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tolist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_topk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trace, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_untyped_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_values, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_var, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_where, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_zero_, test/test_overrides.py::TestTorchFunctionOverride::test_base, test/test_overrides.py::TestTorchFunctionOverride::test_dtype_override, test/test_overrides.py::TestTorchFunctionOverride::test_grad, test/test_overrides.py::TestTorchFunctionOverride::test_has_torch_function_non_sequence, test/test_overrides.py::TestTorchFunctionOverride::test_mean_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_mm_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_pow_rpow, test/test_overrides.py::TestTorchFunctionOverride::test_precedence_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_tensor_subclass_propagation, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cond, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eig, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eigh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eigvals, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eigvalsh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_householder_product, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_inv, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_rank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_multi_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_pinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_solve_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_solve_triangular, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_zeta, test/test_overrides.py::TestTorchFunctionOverride::test_torch__assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__conj_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__functional_assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__fw_primal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lobpcg_lobpcg, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_pca_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_svd_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__make_dual_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__native_batch_norm_legit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__neg_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__reshape_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__rowwise_prune, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sparse_broadcast_to_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_linear_prepack, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_quantized_linear_prepacked, test/test_overrides.py::TestTorchFunctionOverride::test_torch_abs, test/test_overrides.py::TestTorchFunctionOverride::test_torch_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mish, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mse_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_head_attention_forward, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_normalize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_pad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu6, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_silu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_smooth_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softsign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_tanhshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_with_distance_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_constant_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_kaiming_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_torch_norm_except_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nuclear_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_numel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ones_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_outer, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pairwise_distance, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_unshuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polar, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_positive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_channel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor_dynamic, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rand_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randint_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randn_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_torch_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_roll, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_torch_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_saddmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_searchsorted, test/test_overrides.py::TestTorchFunctionOverride::test_torch_segment_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_smm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_square, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_float, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_int, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_ite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_topk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trace, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapz, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tril, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unflatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unfold_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_where, test/test_overrides.py::TestTorchFunctionOverride::test_torch_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_zeros_like, test/test_overrides.py::TestTorchFunctionOverride::test_user_implementation_raises, test/test_overrides.py::TestEinsumOverride::test_wrapper, test/test_overrides.py::TestGradCheckOverride::test_gradcheck, test/test_overrides.py::TestNamedTuple::test_max, test/test_overrides.py::TestGradNewOnesOverride::test_newones, test/test_overrides.py::TestPickle::test_pickle, test/test_overrides.py::TestBroadcastAllOverride::test_broadcast_all, test/test_overrides.py::TestWrapTorchFunction::test_wrap_torch_function, test/test_overrides.py::TestIndexing::test_getitem, test/test_overrides.py::TestIndexing::test_getitem_subclass, test/test_overrides.py::TestIndexing::test_setitem, test/test_overrides.py::TestIndexing::test_setitem_subclass, test/test_overrides.py::TestIndexing::test_setitem_val, test/test_overrides.py::TestIterator::test_iterator, test/test_overrides.py::TestRNN::test_rnn, test/test_overrides.py::TestDisabledTorchFunction::test_parameter_does_not_prevent_dispatch, test/test_overrides.py::TestResolveName::test_resolve_name, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_standalone_class, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_tensor_subclass, test/test_overrides.py::TestDisabledUserWarnings::test_no_implicit_user_warning_for_deprecated_functions, test/test_overrides.py::TestTorchFunctionMode::test_all_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_basic, test/test_overrides.py::TestTorchFunctionMode::test_custom_device_type, test/test_overrides.py::TestTorchFunctionMode::test_device_context_semantics, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_subclass, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_torch_function_ctx, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_mode, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_not_mode, test/test_overrides.py::TestTorchFunctionMode::test_distributions_bernoulli, test/test_overrides.py::TestTorchFunctionMode::test_error_using_class_method_on_mode, test/test_overrides.py::TestTorchFunctionMode::test_factory_override, test/test_overrides.py::TestTorchFunctionMode::test_get_cur_mode, test/test_overrides.py::TestTorchFunctionMode::test_get_mode_stack, test/test_overrides.py::TestTorchFunctionMode::test_getitem_call, test/test_overrides.py::TestTorchFunctionMode::test_mode_notimplemented_loop, test/test_overrides.py::TestTorchFunctionMode::test_modes_handle_first, test/test_overrides.py::TestTorchFunctionMode::test_modes_return_notimplemented, test/test_overrides.py::TestTorchFunctionMode::test_nested_modes_with_python_has_torch_function, test/test_overrides.py::TestTorchFunctionMode::test_nested_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_nn_parse_to, test/test_overrides.py::TestTorchFunctionMode::test_reentrant_mode_idiom, test/test_overrides.py::TestTorchFunctionMode::test_restacking_with_ancestor, test/test_overrides.py::TestTorchFunctionMode::test_subclass_hash, test/test_overrides.py::TestTorchFunctionMode::test_torch_function_all_disabled_api, test/test_overrides.py::TestTorchFunctionMode::test_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes
2025-03-14T04:12:55.4500363Z 
2025-03-14T04:12:55.4500613Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-03-14 04:12:55.357044]
2025-03-14T04:12:58.0090071Z running install
2025-03-14T04:12:58.0091425Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:12:58.0092241Z !!
2025-03-14T04:12:58.0092384Z 
2025-03-14T04:12:58.0092525Z         ********************************************************************************
2025-03-14T04:12:58.0092959Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:12:58.0093387Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:12:58.0093783Z         standards-based tools.
2025-03-14T04:12:58.0093999Z 
2025-03-14T04:12:58.0094315Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:12:58.0094873Z         ********************************************************************************
2025-03-14T04:12:58.0095148Z 
2025-03-14T04:12:58.0095243Z !!
2025-03-14T04:12:58.0095495Z   self.initialize_options()
2025-03-14T04:12:58.0221131Z running build
2025-03-14T04:12:58.0221406Z running build_py
2025-03-14T04:12:58.0297755Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:12:58.0299766Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:12:58.0303030Z running build_ext
2025-03-14T04:12:58.1391056Z building 'torch_test_cpp_extension.cpp' extension
2025-03-14T04:12:58.1391862Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313
2025-03-14T04:12:58.1691088Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-14T04:12:58.1692193Z Compiling objects...
2025-03-14T04:12:58.1692616Z Using envvar MAX_JOBS (14) as the number of workers...
2025-03-14T04:12:58.9590118Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -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-313/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
2025-03-14T04:12:58.9688846Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:12:59.2324905Z building 'torch_test_cpp_extension.maia' extension
2025-03-14T04:12:59.2626506Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-14T04:12:59.2636113Z Compiling objects...
2025-03-14T04:12:59.2636695Z Using envvar MAX_JOBS (14) as the number of workers...
2025-03-14T04:12:59.9952873Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:13:00.0002147Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:13:00.2467557Z building 'torch_test_cpp_extension.rng' extension
2025-03-14T04:13:00.2767250Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-14T04:13:00.2768064Z Compiling objects...
2025-03-14T04:13:00.2768421Z Using envvar MAX_JOBS (14) as the number of workers...
2025-03-14T04:13:01.2008308Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -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-313/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
2025-03-14T04:13:01.2060349Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:13:01.4852403Z running install_lib
2025-03-14T04:13:01.4930427Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-14T04:13:01.4934633Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:13:01.4935769Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:13:01.4937229Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:13:01.4971747Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:13:01.5007176Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:13:01.5048583Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-313.pyc
2025-03-14T04:13:01.5051272Z running install_egg_info
2025-03-14T04:13:01.5226914Z running egg_info
2025-03-14T04:13:01.5295868Z creating torch_test_cpp_extension.egg-info
2025-03-14T04:13:01.5297110Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-14T04:13:01.5300116Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-14T04:13:01.5301797Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-14T04:13:01.5303786Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-14T04:13:01.5304955Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:13:01.5380037Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:13:01.5387402Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:13:01.5388879Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-14T04:13:01.5394775Z running install_scripts
2025-03-14T04:13:03.7540307Z running install
2025-03-14T04:13:03.7541640Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:13:03.7542489Z !!
2025-03-14T04:13:03.7542633Z 
2025-03-14T04:13:03.7542773Z         ********************************************************************************
2025-03-14T04:13:03.7543194Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:13:03.7543925Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:13:03.7544332Z         standards-based tools.
2025-03-14T04:13:03.7544528Z 
2025-03-14T04:13:03.7544855Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:13:03.7545429Z         ********************************************************************************
2025-03-14T04:13:03.7545681Z 
2025-03-14T04:13:03.7545790Z !!
2025-03-14T04:13:03.7546041Z   self.initialize_options()
2025-03-14T04:13:03.7672530Z running build
2025-03-14T04:13:03.7672809Z running build_ext
2025-03-14T04:13:03.8752400Z building 'no_python_abi_suffix_test' extension
2025-03-14T04:13:03.8754922Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313
2025-03-14T04:13:03.9052845Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-14T04:13:03.9053698Z Compiling objects...
2025-03-14T04:13:03.9054047Z Using envvar MAX_JOBS (14) as the number of workers...
2025-03-14T04:13:03.9850800Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -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-313/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
2025-03-14T04:13:03.9894497Z creating build/lib.linux-x86_64-cpython-313
2025-03-14T04:13:03.9899086Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so
2025-03-14T04:13:04.0487258Z running install_lib
2025-03-14T04:13:04.0565875Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-14T04:13:04.0568900Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-14T04:13:04.0573663Z running install_egg_info
2025-03-14T04:13:04.0747819Z running egg_info
2025-03-14T04:13:04.0815285Z creating no_python_abi_suffix_test.egg-info
2025-03-14T04:13:04.0816162Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2025-03-14T04:13:04.0819346Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2025-03-14T04:13:04.0821452Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2025-03-14T04:13:04.0822634Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-14T04:13:04.0894198Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-14T04:13:04.0900787Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-14T04:13:04.0902189Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info
2025-03-14T04:13:04.0906923Z running install_scripts
2025-03-14T04:13:04.5503886Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:13:04.5506271Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:13:04.550397]
2025-03-14T04:13:08.0134074Z 
2025-03-14T04:13:08.0135034Z test_cpp_extensions_aot_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_ninja_1.1_74af050679a89889_.log
2025-03-14T04:13:08.0142792Z Running 20 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_libtorch_agnostic, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library
2025-03-14T04:13:08.0149860Z 
2025-03-14T04:13:08.0150123Z Running inductor/test_distributed_patterns 1/1 ... [2025-03-14 04:13:08.013641]
2025-03-14T04:13:08.0150638Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:13:08.0151777Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_distributed_patterns.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:13:08.013916]
2025-03-14T04:13:48.8841786Z 
2025-03-14T04:13:48.8842903Z inductor/test_distributed_patterns 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_distributed_patterns_1.1_836e7cee9db919da_.log
2025-03-14T04:13:48.8853285Z Running 20 items in this shard: test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_aot_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_nested_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_aot, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_multi_layers, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return3, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return4, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter2
2025-03-14T04:13:48.8862513Z 
2025-03-14T04:13:48.8862737Z Running test_cpp_api_parity 1/1 ... [2025-03-14 04:13:48.884383]
2025-03-14T04:13:48.8863171Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:13:48.8864224Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:13:48.884705]
2025-03-14T04:13:58.3617628Z 
2025-03-14T04:13:58.3618610Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_e5766069446d6af1_.log
2025-03-14T04:13:58.3806089Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda
2025-03-14T04:13:58.3989815Z 
2025-03-14T04:13:58.3990074Z Running inductor/test_benchmark_fusion 1/1 ... [2025-03-14 04:13:58.362522]
2025-03-14T04:13:58.3990572Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:13:58.3991694Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_benchmark_fusion.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:13:58.362798]
2025-03-14T04:14:15.9499701Z 
2025-03-14T04:14:15.9500738Z inductor/test_benchmark_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_benchmark_fusion_1.1_fdbaf52689a68609_.log
2025-03-14T04:14:15.9503895Z Running 6 items in this shard: test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_avoid_register_spilling_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_foreach_kernel_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_register_spills_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_resnet18_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_softmax_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_tield_kernel_fusion_cpu
2025-03-14T04:14:15.9506364Z 
2025-03-14T04:14:15.9506570Z Running test_extension_utils 1/1 ... [2025-03-14 04:14:15.950132]
2025-03-14T04:14:15.9507018Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:14:15.9508305Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_extension_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:14:15.950450]
2025-03-14T04:14:19.3690358Z 
2025-03-14T04:14:19.3691067Z test_extension_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_extension_utils_1.1_bff5aee6e0f3a52e_.log
2025-03-14T04:14:19.3692616Z Running 2 items in this shard: test/test_extension_utils.py::TestExtensionUtils::test_external_module_register, test/test_extension_utils.py::TestExtensionUtils::test_external_module_register_with_renamed_backend
2025-03-14T04:14:19.3693714Z 
2025-03-14T04:14:19.3694225Z Running inductor/test_cutlass_backend 1/1 ... [2025-03-14 04:14:19.369212]
2025-03-14T04:14:19.3694783Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:14:19.3697736Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cutlass_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:14:19.369518]
2025-03-14T04:14:26.0636543Z 
2025-03-14T04:14:26.0637719Z inductor/test_cutlass_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cutlass_backend_1.1_d30ffc779282d629_.log
2025-03-14T04:14:26.0638545Z 
2025-03-14T04:14:26.0639300Z Running test_show_pickle 1/1 ... [2025-03-14 04:14:26.063785]
2025-03-14T04:14:26.0639735Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:14:26.0642644Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:14:26.064080]
2025-03-14T04:14:29.4827545Z 
2025-03-14T04:14:29.4828431Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_0d1ab342d21eb52c_.log
2025-03-14T04:14:29.4829412Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model
2025-03-14T04:14:29.4829851Z 
2025-03-14T04:14:29.4831264Z Running test_transformers_privateuse1 1/1 ... [2025-03-14 04:14:29.482943]
2025-03-14T04:14:30.9710743Z running install
2025-03-14T04:14:30.9711638Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:14:30.9712460Z !!
2025-03-14T04:14:30.9712582Z 
2025-03-14T04:14:30.9712733Z         ********************************************************************************
2025-03-14T04:14:30.9713513Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:14:30.9713946Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:14:30.9714327Z         standards-based tools.
2025-03-14T04:14:30.9714539Z 
2025-03-14T04:14:30.9714853Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:14:30.9715400Z         ********************************************************************************
2025-03-14T04:14:30.9715663Z 
2025-03-14T04:14:30.9715753Z !!
2025-03-14T04:14:30.9715992Z   self.initialize_options()
2025-03-14T04:14:30.9842067Z running build
2025-03-14T04:14:30.9842337Z running build_py
2025-03-14T04:14:30.9919511Z creating build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:30.9921406Z copying pytorch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:30.9923585Z copying pytorch_openreg/_aten_impl.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:30.9925183Z copying pytorch_openreg/_device_daemon.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:30.9926462Z copying pytorch_openreg/_meta_parser.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:30.9932293Z running build_ext
2025-03-14T04:14:31.1013146Z building 'pytorch_openreg._C' extension
2025-03-14T04:14:31.1016021Z creating /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc
2025-03-14T04:14:31.1318314Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-14T04:14:31.1319166Z Compiling objects...
2025-03-14T04:14:31.1319582Z Using envvar MAX_JOBS (14) as the number of workers...
2025-03-14T04:14:31.4739919Z [1/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:14:31.5000753Z [2/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:14:31.5033214Z [3/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:14:31.5085548Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so
2025-03-14T04:14:31.7877104Z running install_lib
2025-03-14T04:14:31.7955101Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-14T04:14:31.7958275Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-14T04:14:31.7959694Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-14T04:14:31.7961278Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_aten_impl.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-14T04:14:31.7963104Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_device_daemon.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-14T04:14:31.7964614Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_meta_parser.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-14T04:14:31.7966083Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-14T04:14:31.8015312Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/__init__.py to __init__.cpython-313.pyc
2025-03-14T04:14:31.8018645Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_aten_impl.py to _aten_impl.cpython-313.pyc
2025-03-14T04:14:31.8035194Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_device_daemon.py to _device_daemon.cpython-313.pyc
2025-03-14T04:14:31.8064415Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_meta_parser.py to _meta_parser.cpython-313.pyc
2025-03-14T04:14:31.8072812Z running install_egg_info
2025-03-14T04:14:31.8244106Z running egg_info
2025-03-14T04:14:31.8312671Z creating pytorch_openreg.egg-info
2025-03-14T04:14:31.8313498Z writing pytorch_openreg.egg-info/PKG-INFO
2025-03-14T04:14:31.8317159Z writing dependency_links to pytorch_openreg.egg-info/dependency_links.txt
2025-03-14T04:14:31.8319290Z writing requirements to pytorch_openreg.egg-info/requires.txt
2025-03-14T04:14:31.8320085Z writing top-level names to pytorch_openreg.egg-info/top_level.txt
2025-03-14T04:14:31.8321460Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-14T04:14:31.8396762Z reading manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-14T04:14:31.8404217Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-14T04:14:31.8405469Z Copying pytorch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg-1.0-py3.13.egg-info
2025-03-14T04:14:31.8411484Z running install_scripts
2025-03-14T04:14:32.3126491Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:14:32.3129518Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers_privateuse1.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:14:32.312725]
2025-03-14T04:14:39.8369916Z 
2025-03-14T04:14:39.8370887Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_8721869711950676_.log
2025-03-14T04:14:39.8373272Z Running 3 items in this shard: test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_fused_sdp_choice_privateuseone, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable_backward
2025-03-14T04:14:39.8374918Z 
2025-03-14T04:14:39.8375253Z Running test_cpp_extensions_open_device_registration 1/1 ... [2025-03-14 04:14:39.837137]
2025-03-14T04:14:41.3338408Z running install
2025-03-14T04:14:41.3339293Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:14:41.3340089Z !!
2025-03-14T04:14:41.3340252Z 
2025-03-14T04:14:41.3340387Z         ********************************************************************************
2025-03-14T04:14:41.3340793Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:14:41.3341214Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:14:41.3341602Z         standards-based tools.
2025-03-14T04:14:41.3341811Z 
2025-03-14T04:14:41.3342122Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:14:41.3342670Z         ********************************************************************************
2025-03-14T04:14:41.3342932Z 
2025-03-14T04:14:41.3343281Z !!
2025-03-14T04:14:41.3343526Z   self.initialize_options()
2025-03-14T04:14:41.3470438Z running build
2025-03-14T04:14:41.3470722Z running build_py
2025-03-14T04:14:41.3548329Z creating build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:41.3550321Z copying pytorch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:41.3551945Z copying pytorch_openreg/_aten_impl.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:41.3553505Z copying pytorch_openreg/_device_daemon.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:41.3554830Z copying pytorch_openreg/_meta_parser.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-14T04:14:41.3560649Z running build_ext
2025-03-14T04:14:41.4643052Z building 'pytorch_openreg._C' extension
2025-03-14T04:14:41.4646580Z creating /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc
2025-03-14T04:14:41.4946907Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-14T04:14:41.4947793Z Compiling objects...
2025-03-14T04:14:41.4948378Z Using envvar MAX_JOBS (14) as the number of workers...
2025-03-14T04:14:41.8039080Z [1/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:14:41.8256166Z [2/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:14:41.8373051Z [3/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:14:41.8425391Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so
2025-03-14T04:14:42.1234429Z running install_lib
2025-03-14T04:14:42.1316024Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-14T04:14:42.1372227Z running install_egg_info
2025-03-14T04:14:42.1543999Z running egg_info
2025-03-14T04:14:42.1613849Z writing pytorch_openreg.egg-info/PKG-INFO
2025-03-14T04:14:42.1617746Z writing dependency_links to pytorch_openreg.egg-info/dependency_links.txt
2025-03-14T04:14:42.1629442Z writing requirements to pytorch_openreg.egg-info/requires.txt
2025-03-14T04:14:42.1639086Z writing top-level names to pytorch_openreg.egg-info/top_level.txt
2025-03-14T04:14:42.1724197Z reading manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-14T04:14:42.1733694Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-14T04:14:42.1745737Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg-1.0-py3.13.egg-info' (and everything under it)
2025-03-14T04:14:42.1747724Z Copying pytorch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg-1.0-py3.13.egg-info
2025-03-14T04:14:42.1754720Z running install_scripts
2025-03-14T04:14:42.6656192Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:14:42.6658838Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_open_device_registration.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:14:42.665655]
2025-03-14T04:14:56.1482817Z 
2025-03-14T04:14:56.1483943Z test_cpp_extensions_open_device_registration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_open_device_registration_1.1_062851176bda5368_.log
2025-03-14T04:14:56.1497200Z Running 23 items in this shard: test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_base_device_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_common_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_aliasing, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_returns_self, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_cpu_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_dispatchstub, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_dlpack, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_faketensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_generator_registration_and_hooks, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_named_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_numpy_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_packed_sequence, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_quantized, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_random, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_scalar_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_pin_memory, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_resize, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_type, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensorlist_type_fallback
2025-03-14T04:14:56.1509580Z 
2025-03-14T04:14:56.1509827Z Running test_namedtuple_return_api 1/1 ... [2025-03-14 04:14:56.148468]
2025-03-14T04:14:56.1510297Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:14:56.1511382Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtuple_return_api.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:14:56.148726]
2025-03-14T04:15:00.4686022Z 
2025-03-14T04:15:00.4686803Z test_namedtuple_return_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtuple_return_api_1.1_0e8ea024dbaaec2c_.log
2025-03-14T04:15:00.4689027Z Running 3 items in this shard: test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_import_return_types, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_namedtuple_return, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_native_functions_yaml
2025-03-14T04:15:00.4690209Z 
2025-03-14T04:15:00.4690419Z Running test_tensorexpr 1/1 ... [2025-03-14 04:15:00.468749]
2025-03-14T04:15:00.4690850Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:15:00.4692008Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorexpr.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:15:00.469014]
2025-03-14T04:15:48.9464970Z 
2025-03-14T04:15:48.9465950Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_c5bfcf6cc1e5377c_.log
2025-03-14T04:15:48.9486896Z Running 74 items in this shard: test/test_tensorexpr.py::TestTensorExprFuser::test_add_const_rhs, test/test_tensorexpr.py::TestTensorExprFuser::test_add_sub, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_input_and_module, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_module, test/test_tensorexpr.py::TestTensorExprFuser::test_all_combos, test/test_tensorexpr.py::TestTensorExprFuser::test_alpha, test/test_tensorexpr.py::TestTensorExprFuser::test_binary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_bitwise_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast3, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_2, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_big2, test/test_tensorexpr.py::TestTensorExprFuser::test_cat, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_empty_tensors, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_negative_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_only, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_promote_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_with_constant_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_char, test/test_tensorexpr.py::TestTensorExprFuser::test_chunk, test/test_tensorexpr.py::TestTensorExprFuser::test_clamp, test/test_tensorexpr.py::TestTensorExprFuser::test_constant, test/test_tensorexpr.py::TestTensorExprFuser::test_double, test/test_tensorexpr.py::TestTensorExprFuser::test_double_intrinsics, test/test_tensorexpr.py::TestTensorExprFuser::test_dynamic_shape, test/test_tensorexpr.py::TestTensorExprFuser::test_easy, test/test_tensorexpr.py::TestTensorExprFuser::test_eq, test/test_tensorexpr.py::TestTensorExprFuser::test_exp_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_four_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_ge, test/test_tensorexpr.py::TestTensorExprFuser::test_gt, test/test_tensorexpr.py::TestTensorExprFuser::test_guard_fails, test/test_tensorexpr.py::TestTensorExprFuser::test_half_bn_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_half_gelu, test/test_tensorexpr.py::TestTensorExprFuser::test_int64_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_int_output, test/test_tensorexpr.py::TestTensorExprFuser::test_le, test/test_tensorexpr.py::TestTensorExprFuser::test_loop, test/test_tensorexpr.py::TestTensorExprFuser::test_lt, test/test_tensorexpr.py::TestTensorExprFuser::test_mask, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction2, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1_2, test/test_tensorexpr.py::TestTensorExprFuser::test_multi_rand, test/test_tensorexpr.py::TestTensorExprFuser::test_multioutput, test/test_tensorexpr.py::TestTensorExprFuser::test_multiple_outputs, test/test_tensorexpr.py::TestTensorExprFuser::test_nans, test/test_tensorexpr.py::TestTensorExprFuser::test_ne, test/test_tensorexpr.py::TestTensorExprFuser::test_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_propagated_mem_layout, test/test_tensorexpr.py::TestTensorExprFuser::test_rand_like, test/test_tensorexpr.py::TestTensorExprFuser::test_rank_two, test/test_tensorexpr.py::TestTensorExprFuser::test_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_remainder, test/test_tensorexpr.py::TestTensorExprFuser::test_reps, test/test_tensorexpr.py::TestTensorExprFuser::test_round_2, test/test_tensorexpr.py::TestTensorExprFuser::test_scalar, test/test_tensorexpr.py::TestTensorExprFuser::test_short, test/test_tensorexpr.py::TestTensorExprFuser::test_simple_add, test/test_tensorexpr.py::TestTensorExprFuser::test_sin_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_slice, test/test_tensorexpr.py::TestTensorExprFuser::test_sliced_stride, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cpu, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cuda, test/test_tensorexpr.py::TestTensorExprFuser::test_strided_output_preserved, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg2, test/test_tensorexpr.py::TestTensorExprFuser::test_transpose, test/test_tensorexpr.py::TestTensorExprFuser::test_unary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_unsqueeze, test/test_tensorexpr.py::TestTensorExprFuser::test_where
2025-03-14T04:15:48.9506437Z 
2025-03-14T04:15:48.9506603Z Running test_utils 1/1 ... [2025-03-14 04:15:48.946780]
2025-03-14T04:15:48.9507001Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:15:48.9508022Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:15:48.947105]
2025-03-14T04:18:59.5107956Z 
2025-03-14T04:18:59.5110419Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_4835e74f3e61cf8a_.log
2025-03-14T04:18:59.7196917Z Running 6028 items in this shard: test/test_utils.py::TestCheckpoint::test_checkpoint, test/test_utils.py::TestCheckpoint::test_checkpoint_module_list, test/test_utils.py::TestCheckpoint::test_checkpoint_no_tensors, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs, test/test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant, test/test_utils.py::TestCheckpoint::test_checkpoint_partial_grad, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args, test/test_utils.py::TestCheckpoint::test_checkpoint_trigger, test/test_utils.py::TestCheckpoint::test_checkpoint_valid, test/test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free, test/test_utils.py::TestCheckpoint::test_get_device_states_recursive, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda, test/test_utils.py::TestDataLoaderUtils::test_multi_drop, test/test_utils.py::TestDataLoaderUtils::test_multi_keep, test/test_utils.py::TestDataLoaderUtils::test_random_seed, test/test_utils.py::TestDataLoaderUtils::test_single_drop, test/test_utils.py::TestDataLoaderUtils::test_single_keep, test/test_utils.py::TestBottleneck::test_bottleneck_cpu_only, test/test_utils.py::TestBottleneck::test_bottleneck_cuda, test/test_utils.py::TestCollectEnv::test_smoke, test/test_utils.py::TestONNXUtils::test_check_onnx_broadcast, test/test_utils.py::TestONNXUtils::test_prepare_onnx_paddings, test/test_utils.py::TestHipify::test_import_hipify, test/test_utils.py::TestHipifyTrie::test_add_and_search_trie, test/test_utils.py::TestHipifyTrie::test_add_multiple_and_search_trie, test/test_utils.py::TestHipifyTrie::test_char_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_prefix_words_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_quote_escape, test/test_utils.py::TestHipifyTrie::test_single_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_special_char_export_trie_to_regex, test/test_utils.py::TestAssert::test_assert_scriptable, test/test_utils.py::TestAssert::test_assert_true, test/test_utils.py::TestStandaloneCPPJIT::test_load_standalone, test/test_utils.py::TestRenderUtils::test_basic, test/test_utils.py::TestDeviceUtilsCPU::test_basic_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_generator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_uint8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_one_hot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu, test/test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok, test/test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok, test/test_utils.py::TestTraceback::test_basic, test/test_utils.py::TestTraceback::test_captured_traceback, test/test_utils.py::TestTraceback::test_captured_traceback_format_all, test/test_utils.py::TestTraceback::test_captured_traceback_format_all_cached, test/test_utils.py::TestTraceback::test_format_traceback_short, test/test_utils.py::TestTryImport::test_import_existing, test/test_utils.py::TestTryImport::test_import_imported, test/test_utils.py::TestTryImport::test_import_missing
2025-03-14T04:18:59.9529357Z 
2025-03-14T04:18:59.9529782Z Running test_multiprocessing 1/1 ... [2025-03-14 04:18:59.517562]
2025-03-14T04:18:59.9530623Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:18:59.9532560Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:18:59.517883]
2025-03-14T04:20:00.7640397Z 
2025-03-14T04:20:00.7641359Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_59bc3d269e023b6c_.log
2025-03-14T04:20:00.7655646Z Running 41 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_meta_simple, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork
2025-03-14T04:20:00.7669060Z 
2025-03-14T04:20:00.7669231Z Running test_torch 1/1 ... [2025-03-14 04:20:00.764241]
2025-03-14T04:20:00.7669643Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:20:00.7670664Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:20:00.764526]
2025-03-14T04:20:17.6010326Z 
2025-03-14T04:20:17.6011198Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_e0c68f46ea018437_.log
2025-03-14T04:20:17.6328848Z Running 1035 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_conj_physical_meta_stride, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_64bit_indexing_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_FractionalMaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float32, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_uint8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_shift_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_skip_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_False_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_True_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu
2025-03-14T04:20:17.6641276Z 
2025-03-14T04:20:17.6641465Z Running test_autocast 1/1 ... [2025-03-14 04:20:17.602139]
2025-03-14T04:20:17.6641912Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:20:17.6642952Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autocast.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:20:17.602417]
2025-03-14T04:20:21.6222927Z 
2025-03-14T04:20:21.6223817Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_a0fd4ecd100f9a04_.log
2025-03-14T04:20:21.6230108Z Running 20 items in this shard: test/test_autocast.py::TestAutocastCPU::test_autocast_disabled_with_fp32_dtype, test/test_autocast.py::TestAutocastCPU::test_autocast_methods_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_16, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_rnn, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_16, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_need_autocast_promote, test/test_autocast.py::TestAutocastCPU::test_cpu_autocast_deprecated_warning, test/test_autocast.py::TestAutocastCPU::test_generic_autocast, test/test_autocast.py::TestAutocastGPU::test_autocast_prioritize, test/test_autocast.py::TestAutocastGPU::test_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_bfloat16_supported, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_error_message, test/test_autocast.py::TestTorchAutocast::test_autocast_fast_dtype, test/test_autocast.py::TestTorchAutocast::test_invalid_device, test/test_autocast.py::TestTorchAutocast::test_non_string_device
2025-03-14T04:20:21.6236307Z 
2025-03-14T04:20:21.6236545Z Running test_autograd_fallback 1/1 ... [2025-03-14 04:20:21.622308]
2025-03-14T04:20:21.6237003Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:20:21.6238090Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autograd_fallback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:20:21.622607]
2025-03-14T04:20:25.1414514Z 
2025-03-14T04:20:25.1415514Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_f7aca90a0aaf7f00_.log
2025-03-14T04:20:25.1428342Z Running 28 items in this shard: test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_warn
2025-03-14T04:20:25.1439992Z 
2025-03-14T04:20:25.1440201Z Running test_python_dispatch 1/1 ... [2025-03-14 04:20:25.141648]
2025-03-14T04:20:25.1440647Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:20:25.1441710Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_python_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:20:25.141935]
2025-03-14T04:20:30.4132099Z 
2025-03-14T04:20:30.4133076Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_92372228daca4f05_.log
2025-03-14T04:20:30.4176373Z Running 114 items in this shard: test/test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed, test/test_python_dispatch.py::TestPythonRegistration::test_alias_analysis, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_fallback, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset, test/test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls, test/test_python_dispatch.py::TestPythonRegistration::test_finalizer, test/test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries, test/test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum, test/test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator, test/test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint, test/test_python_dispatch.py::TestPythonDispatch::test_all_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr, test/test_python_dispatch.py::TestPythonDispatch::test_basic, test/test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor, test/test_python_dispatch.py::TestPythonDispatch::test_custom_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode, test/test_python_dispatch.py::TestPythonDispatch::test_exception_handling, test/test_python_dispatch.py::TestPythonDispatch::test_fancy_strides, test/test_python_dispatch.py::TestPythonDispatch::test_format, test/test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode, test/test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack, test/test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_invalid_ret, test/test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default, test/test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_like, test/test_python_dispatch.py::TestPythonDispatch::test_list_ret, test/test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata, test/test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug, test/test_python_dispatch.py::TestPythonDispatch::test_mode_detection, test/test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode, test/test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_new_ones, test/test_python_dispatch.py::TestPythonDispatch::test_none_wrapping, test/test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode, test/test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list, test/test_python_dispatch.py::TestPythonDispatch::test_out, test/test_python_dispatch.py::TestPythonDispatch::test_produce_real_type, test/test_python_dispatch.py::TestPythonDispatch::test_record_stream, test/test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride, test/test_python_dispatch.py::TestPythonDispatch::test_return_stream, test/test_python_dispatch.py::TestPythonDispatch::test_set_data, test/test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach, test/test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_storage, test/test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object, test/test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_autograd_device_check, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_creation, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors, test/test_python_dispatch.py::TestPythonDispatch::test_version, test/test_python_dispatch.py::TestPythonDispatch::test_view_returns_alias_under_torch_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately, test/test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_reentrant_dispatch_with_mode, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes, test/test_python_dispatch.py::TestPythonDispatcher::test_basic, test/test_python_dispatch.py::TestPythonDispatcher::test_lstsq, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_fft_fft2_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32
2025-03-14T04:20:30.4218166Z 
2025-03-14T04:20:30.4218369Z Running test_jit_disabled 1/1 ... [2025-03-14 04:20:30.413516]
2025-03-14T04:20:30.4218796Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:20:30.4219846Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_disabled.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:20:30.413857]
2025-03-14T04:20:33.9328152Z 
2025-03-14T04:20:33.9329471Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_69ce2788b3149337_.log
2025-03-14T04:20:33.9331347Z Running 3 items in this shard: test/test_jit_disabled.py::TestJitDisabled::test_attribute, test/test_jit_disabled.py::TestJitDisabled::test_recursive_script, test/test_jit_disabled.py::TestJitDisabled::test_script_module_construction
2025-03-14T04:20:33.9332837Z 
2025-03-14T04:20:33.9333067Z Running test_fake_tensor 1/1 ... [2025-03-14 04:20:33.932798]
2025-03-14T04:20:33.9333491Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:20:33.9334543Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fake_tensor.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:20:33.933096]
2025-03-14T04:20:44.1108532Z 
2025-03-14T04:20:44.1109531Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_252cba9d64e84b8a_.log
2025-03-14T04:20:44.1215666Z Running 268 items in this shard: test/test_fake_tensor.py::FakeTensorTest::test__adaptive_avg_pool2d_backward, test/test_fake_tensor.py::FakeTensorTest::test_alias_call, test/test_fake_tensor.py::FakeTensorTest::test_allow_meta, test/test_fake_tensor.py::FakeTensorTest::test_aten_copy_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_index_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_slice_scatter_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_basic, test/test_fake_tensor.py::FakeTensorTest::test_batch_tensor, test/test_fake_tensor.py::FakeTensorTest::test_binary_op_type_promotion, test/test_fake_tensor.py::FakeTensorTest::test_constructor, test/test_fake_tensor.py::FakeTensorTest::test_convert_fake_to_real, test/test_fake_tensor.py::FakeTensorTest::test_cpu_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cuda_initialized, test/test_fake_tensor.py::FakeTensorTest::test_cuda_lstm, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_with_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_without_fallback, test/test_fake_tensor.py::FakeTensorTest::test_custom_op_fallback, test/test_fake_tensor.py::FakeTensorTest::test_data_dependent_operator, test/test_fake_tensor.py::FakeTensorTest::test_deepcopy, test/test_fake_tensor.py::FakeTensorTest::test_device_inplace_copy, test/test_fake_tensor.py::FakeTensorTest::test_embedding_bag_meta, test/test_fake_tensor.py::FakeTensorTest::test_export_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fake_dispatch_keys, test/test_fake_tensor.py::FakeTensorTest::test_fake_grad_copy, test/test_fake_tensor.py::FakeTensorTest::test_fake_mode_error, test/test_fake_tensor.py::FakeTensorTest::test_from_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fsdp_flat_param, test/test_fake_tensor.py::FakeTensorTest::test_full, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex128, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int16, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int8, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_uint8, test/test_fake_tensor.py::FakeTensorTest::test_index_put_error, test/test_fake_tensor.py::FakeTensorTest::test_jagged_fake_to_fake_preserved, test/test_fake_tensor.py::FakeTensorTest::test_like_constructor, test/test_fake_tensor.py::FakeTensorTest::test_mixed_real_and_fake_inputs, test/test_fake_tensor.py::FakeTensorTest::test_mode, test/test_fake_tensor.py::FakeTensorTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorTest::test_new, test/test_fake_tensor.py::FakeTensorTest::test_non_kwarg_device, test/test_fake_tensor.py::FakeTensorTest::test_non_overlapping_stride_zero, test/test_fake_tensor.py::FakeTensorTest::test_non_parameter_grad, test/test_fake_tensor.py::FakeTensorTest::test_normalize_device, test/test_fake_tensor.py::FakeTensorTest::test_out_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_parameter_instantiation, test/test_fake_tensor.py::FakeTensorTest::test_parameter_view, test/test_fake_tensor.py::FakeTensorTest::test_print_in_fake_mode, test/test_fake_tensor.py::FakeTensorTest::test_randperm, test/test_fake_tensor.py::FakeTensorTest::test_recursive_invocation, test/test_fake_tensor.py::FakeTensorTest::test_repr, test/test_fake_tensor.py::FakeTensorTest::test_same_shape_env_preserved, test/test_fake_tensor.py::FakeTensorTest::test_scalar_inputs, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_False, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_True, test/test_fake_tensor.py::FakeTensorTest::test_setitem, test/test_fake_tensor.py::FakeTensorTest::test_shape_take_not_device, test/test_fake_tensor.py::FakeTensorTest::test_split_return_self, test/test_fake_tensor.py::FakeTensorTest::test_throw, test/test_fake_tensor.py::FakeTensorTest::test_tolist, test/test_fake_tensor.py::FakeTensorTest::test_type_as, test/test_fake_tensor.py::FakeTensorTest::test_unsqueeze_copy, test/test_fake_tensor.py::FakeTensorTest::test_upsample_bilinear_small_channels, test/test_fake_tensor.py::FakeTensorTest::test_zero_dim, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test__adaptive_avg_pool2d_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_alias_call_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_allow_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_copy_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_index_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_slice_scatter_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_basic_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_batch_tensor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_binary_op_type_promotion_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_convert_fake_to_real_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cpu_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_initialized_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_lstm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_with_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_without_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_custom_op_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_data_dependent_operator_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_deepcopy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_device_inplace_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_embedding_bag_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_export_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_dispatch_keys_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_grad_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_from_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fsdp_flat_param_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_full_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex128_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int16_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_uint8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_put_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_jagged_fake_to_fake_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_like_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mixed_real_and_fake_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_overlapping_stride_zero_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_parameter_grad_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_normalize_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_out_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_instantiation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_print_in_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_randperm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_recursive_invocation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_repr_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_same_shape_env_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scalar_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_False_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_True_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_setitem_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_shape_take_not_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_split_return_self_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_throw_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_tolist_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_type_as_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unsqueeze_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_upsample_bilinear_small_channels_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_zero_dim_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorConstHandling::test_aliased_const_write, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_propagate_through_functions, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_batch_norm_cpu, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_in_intlist_repro, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_add, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_view_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storage_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storages, test/test_fake_tensor.py::FakeTensorConstHandling::test_simple, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_aliased_const_write_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_propagate_through_functions_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_batch_norm_cpu_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_in_intlist_repro_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_add_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_view_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storage_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storages_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_simple_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_key, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_weak_ref, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_from_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_to_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_multiple_modes, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_active_mode, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_ref_cycle, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_mode_error, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_non_view, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_view, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_key_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_weak_ref_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_from_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_to_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_multiple_modes_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_active_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_ref_cycle_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_non_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_view_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_conv_c1_backward, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_cross_entropy_loss, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_embedding_bag_private, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_fake_gpu_no_init, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_flash_attention, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_like_ops, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_no_dispatch_with_like_function, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_non_kwarg_only_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_sparse_new, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_str_storage, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_new, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_conv_c1_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_cross_entropy_loss_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_embedding_bag_private_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_fake_gpu_no_init_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_flash_attention_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_like_ops_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_no_dispatch_with_like_function_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_non_kwarg_only_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_sparse_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_str_storage_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_new_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args, test/test_fake_tensor.py::FakeTensorPropTest::test_nonzero_stride, test/test_fake_tensor.py::FakeTensorPropTest::test_torch_load_with_fake_mode, test/test_fake_tensor.py::FakeTensorPropTest::test_unbacked_shape_realloc, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_nonzero_stride_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_torch_load_with_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_unbacked_shape_realloc_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization_with_tracing, test/test_fake_tensor.py::FakeTensorDispatchCache::test__upsample_bilinear2d_aa_backward_dynamic_shapes, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_bypass, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_dispatch_key_set, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_hit, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_inplace_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_constants, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_conj, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_inference, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_neg, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_memory_format, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_requires_grad, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_shape, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_storage_offset, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_stride, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_tuple_outputs, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_view_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_fft_hfft2_issue145522, test/test_fake_tensor.py::FakeTensorDispatchCache::test_from_buffer, test/test_fake_tensor.py::FakeTensorDispatchCache::test_inference_mode, test/test_fake_tensor.py::FakeTensorDispatchCache::test_meta_tensor_to_fake_cpu, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device
2025-03-14T04:20:44.1320843Z 
2025-03-14T04:20:44.1321001Z Running test_fx 1/1 ... [2025-03-14 04:20:44.111353]
2025-03-14T04:20:44.1321394Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:20:44.1322403Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:20:44.111638]
2025-03-14T04:24:31.4628961Z 
2025-03-14T04:24:31.4629825Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_136d35bba95eaac3_.log
2025-03-14T04:24:31.5102152Z Running 1257 items in this shard: test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationInput_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_ReturnList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_FactoryFunctionCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_MutationFactory_cpu, test/test_fx.py::TestCSEPass::test_banned_list, test/test_fx.py::TestCSEPass::test_empty, test/test_fx.py::TestCSEPass::test_immutable_list_multiple_entries, test/test_fx.py::TestCSEPass::test_immutable_list_type, test/test_fx.py::TestCSEPass::test_kwarg, test/test_fx.py::TestCSEPass::test_nested_immutable_list_type, test/test_fx.py::TestCSEPass::test_nochange, test/test_fx.py::TestCSEPass::test_rand_like, test/test_fx.py::TestCSEPass::test_rand_n, test/test_fx.py::TestCSEPass::test_random, test/test_fx.py::TestCSEPass::test_simple, test/test_fx.py::TestCSEPass::test_simple_2, test/test_fx.py::TestCSEPass::test_simple_multiple_same_ops, test/test_fx.py::TestCSEPass::test_two_args, test/test_fx.py::TestCSEPass::test_two_args_default, test/test_fx.py::TestDCE::test_dead_chain, test/test_fx.py::TestDCE::test_dead_getattr, test/test_fx.py::TestDCE::test_dead_placeholder, test/test_fx.py::TestDCE::test_dead_placeholder_with_user, test/test_fx.py::TestDCE::test_impure_custom, test/test_fx.py::TestDCE::test_impure_kwargs, test/test_fx.py::TestDCE::test_impure_nodes_args, test/test_fx.py::TestDCE::test_keep_collectives, test/test_fx.py::TestDCE::test_keep_collectives_no_overload, test/test_fx.py::TestDCE::test_keep_module_with_side_effects, test/test_fx.py::TestDCE::test_keep_setitem, test/test_fx.py::TestDCE::test_keep_torch_assert, test/test_fx.py::TestDCE::test_simple, test/test_fx.py::TestConstFold::test_check_inline_non_const, test/test_fx.py::TestConstFold::test_check_inline_non_const_mult_return, test/test_fx.py::TestConstFold::test_check_skip_folding_quant_dequant_pattern, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_no_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_placeholder_reordered, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr_three_input, test/test_fx.py::TestConstFold::test_const_fold_has_inlined_call_module_node, test/test_fx.py::TestConstFold::test_const_fold_module_attr, test/test_fx.py::TestConstFold::test_const_fold_multi_const_folded_attrs, test/test_fx.py::TestConstFold::test_const_fold_noop, test/test_fx.py::TestConstFold::test_const_fold_submod_hierarchy, test/test_fx.py::TestConstFold::test_const_fold_tensor_meta, test/test_fx.py::TestConstFold::test_const_fold_unused_placeholder, test/test_fx.py::TestConstFold::test_dict_output, test/test_fx.py::TestConstFold::test_fold_module, test/test_fx.py::TestConstFold::test_retain_node_meta, test/test_fx.py::TestConstFold::test_three_outputs, test/test_fx.py::TestConstFold::test_two_outputs, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_dim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_ndim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_nelement_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_numel_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_shape_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_size_const, test/test_fx.py::AnnotationsTest::test_annotate, test/test_fx.py::AnnotationsTest::test_annotations, test/test_fx.py::AnnotationsTest::test_broadcasting1, test/test_fx.py::AnnotationsTest::test_broadcasting2, test/test_fx.py::AnnotationsTest::test_broadcasting3, test/test_fx.py::AnnotationsTest::test_consistency, test/test_fx.py::AnnotationsTest::test_precision, test/test_fx.py::TypeCheckerTest::test_flatten_fully_static, test/test_fx.py::TypeCheckerTest::test_resnet50, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast_2, test/test_fx.py::TypeCheckerTest::test_type_check_add_false, test/test_fx.py::TypeCheckerTest::test_type_check_add_true, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_scalar, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_false, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_symbolic, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2_fully_static, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_types, test/test_fx.py::TypeCheckerTest::test_type_check_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_flatten3, test/test_fx.py::TypeCheckerTest::test_type_check_flatten_2, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true_param_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_true, test/test_fx.py::TypeCheckerTest::test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_False, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_true, test/test_fx.py::TypeCheckerTest::test_type_maxpool2d_fully_static, test/test_fx.py::TypeCheckerTest::test_type_typechecl_maxpool2d_3dinput, test/test_fx.py::TypeCheckerTest::test_typecheck_basicblock, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_function, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_module, test/test_fx.py::TestMatcher::test_split_to_graph_and_name_node_map, test/test_fx.py::TestMatcher::test_subgraph_matcher_ignore_literals, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_attributes, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list_bad, test/test_fx.py::TestMatcher::test_variatic_arg_matching, test/test_fx.py::TestPassManager::test_pass_manager, test/test_fx.py::TestPassManager::test_pass_manager_bad_checks, test/test_fx.py::TestPassManager::test_pass_manager_checks, test/test_fx.py::TestPassManager::test_pass_manager_error, test/test_fx.py::TestPassManager::test_this_before_that_pass_constraint, test/test_fx.py::TestPassManager::test_topological_sort, test/test_fx.py::TestSourceMatcher::test_legalize_slice, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_True, test/test_fx.py::TestSubgraphRewriter::test_matching_pattern_with_list_type_arg, test/test_fx.py::TestSubgraphRewriter::test_matching_variable_arguments, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_callback, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_filters, test/test_fx.py::TestSubgraphRewriter::test_replaced_nodes, test/test_fx.py::TestSubgraphRewriter::test_replacement_with_attrs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_call_method, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_local_revert, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_nodes_with_kwargs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_consecutive_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_duplicated_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_multiple_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_overlapping_matches, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_trivial_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_args, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_results, test/test_fx.py::TestFX::test_all_input_nodes, test/test_fx.py::TestFX::test_annotation_with_future, test/test_fx.py::TestFX::test_annotations_empty_tuple, test/test_fx.py::TestFX::test_annotations_with_forward_references, test/test_fx.py::TestFX::test_annotations_with_no_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_internal_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_no_internal_forward_references, test/test_fx.py::TestFX::test_args_kwargs, test/test_fx.py::TestFX::test_args_kwargs_no_self, test/test_fx.py::TestFX::test_assert, test/test_fx.py::TestFX::test_ast_rewriter_reassigns_submodules, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert_with_message, test/test_fx.py::TestFX::test_ast_rewriter_wrap, test/test_fx.py::TestFX::test_ast_rewriter_wrap_fn_directly, test/test_fx.py::TestFX::test_ast_rewriter_wrap_with_submodule, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_autowrap_functions, test/test_fx.py::TestFX::test_concrete_arg_none_assert, test/test_fx.py::TestFX::test_construct_root_dict, test/test_fx.py::TestFX::test_control_flow_tracing, test/test_fx.py::TestFX::test_copy_it, test/test_fx.py::TestFX::test_copy_no_remap, test/test_fx.py::TestFX::test_ctx_mgr, test/test_fx.py::TestFX::test_custom_codegen, test/test_fx.py::TestFX::test_custom_codegen_with_transformer, test/test_fx.py::TestFX::test_custom_import, test/test_fx.py::TestFX::test_custom_proxy_dynamic_value, test/test_fx.py::TestFX::test_custom_proxy_input_dependent_control_flow, test/test_fx.py::TestFX::test_custom_proxy_type, test/test_fx.py::TestFX::test_custom_proxy_type_literal, test/test_fx.py::TestFX::test_custom_traceback_not_raised_when_exception_source_is_submodule, test/test_fx.py::TestFX::test_custom_traceback_raised_when_exception_source_is_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graph_with_tracer_cls, test/test_fx.py::TestFX::test_deepcopy_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graphmodule_with_transform, test/test_fx.py::TestFX::test_deepcopy_no_recursion, test/test_fx.py::TestFX::test_deepcopy_recursion_depth, test/test_fx.py::TestFX::test_deepcopy_tracer, test/test_fx.py::TestFX::test_deepcopy_with_submods_params, test/test_fx.py::TestFX::test_delete_unused_submodules_leaf, test/test_fx.py::TestFX::test_delete_unused_values, test/test_fx.py::TestFX::test_dict, test/test_fx.py::TestFX::test_direct_param_use, test/test_fx.py::TestFX::test_disallow_override, test/test_fx.py::TestFX::test_ellipsis, test/test_fx.py::TestFX::test_empty_graph_codegen, test/test_fx.py::TestFX::test_enum, test/test_fx.py::TestFX::test_erase_node_error, test/test_fx.py::TestFX::test_example_shape_prop, test/test_fx.py::TestFX::test_find_uses, test/test_fx.py::TestFX::test_fn_type_annotation_empty, test/test_fx.py::TestFX::test_fn_type_annotations, test/test_fx.py::TestFX::test_fx_and_or, test/test_fx.py::TestFX::test_fx_create_arg, test/test_fx.py::TestFX::test_fx_shifts, test/test_fx.py::TestFX::test_fx_stateless, test/test_fx.py::TestFX::test_get_torch_func_signature, test/test_fx.py::TestFX::test_getitem, test/test_fx.py::TestFX::test_getitem_subproc, test/test_fx.py::TestFX::test_graph_edit_with_proxy, test/test_fx.py::TestFX::test_graph_fns, test/test_fx.py::TestFX::test_graph_module, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_dict_init, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_mod_init, test/test_fx.py::TestFX::test_graph_module_replicate_for_dp, test/test_fx.py::TestFX::test_graph_unique_names, test/test_fx.py::TestFX::test_graph_unique_names_manual, test/test_fx.py::TestFX::test_immutable_dict_pytree_ops, test/test_fx.py::TestFX::test_immutable_list_pytree_ops, test/test_fx.py::TestFX::test_imul_code_print, test/test_fx.py::TestFX::test_inf_nan, test/test_fx.py::TestFX::test_inf_nan_kwds, test/test_fx.py::TestFX::test_informative_co_filename, test/test_fx.py::TestFX::test_inline_graph, test/test_fx.py::TestFX::test_insert_arg, test/test_fx.py::TestFX::test_insertion_point, test/test_fx.py::TestFX::test_interpreter, test/test_fx.py::TestFX::test_interpreter_default_args, test/test_fx.py::TestFX::test_interpreter_gc_values, test/test_fx.py::TestFX::test_interpreter_noop_resnet18, test/test_fx.py::TestFX::test_interpreter_not_enough_args, test/test_fx.py::TestFX::test_interpreter_onthefly_swap, test/test_fx.py::TestFX::test_interpreter_other_graph, test/test_fx.py::TestFX::test_interpreter_partial_eval, test/test_fx.py::TestFX::test_interpreter_run_node_override, test/test_fx.py::TestFX::test_interpreter_star_args, test/test_fx.py::TestFX::test_interpreter_with_codegen, test/test_fx.py::TestFX::test_layout, test/test_fx.py::TestFX::test_leaf_module, test/test_fx.py::TestFX::test_lineno_map, test/test_fx.py::TestFX::test_matmul_tracing, test/test_fx.py::TestFX::test_metadata_on_ph, test/test_fx.py::TestFX::test_module_deepcopy_edit_nodes, test/test_fx.py::TestFX::test_move_before, test/test_fx.py::TestFX::test_multi_insert_point, test/test_fx.py::TestFX::test_multiple_default_args, test/test_fx.py::TestFX::test_named_tuple_inlined, test/test_fx.py::TestFX::test_namedtuple_return_qualname, test/test_fx.py::TestFX::test_namedtuple_return_trace, test/test_fx.py::TestFX::test_native_callable, test/test_fx.py::TestFX::test_nn_module_stack, test/test_fx.py::TestFX::test_no_mutation, test/test_fx.py::TestFX::test_node_tagging, test/test_fx.py::TestFX::test_nonetype_annotation, test/test_fx.py::TestFX::test_partial_trace, test/test_fx.py::TestFX::test_pickle_custom_import, test/test_fx.py::TestFX::test_pickle_graphmodule, test/test_fx.py::TestFX::test_pickle_nonetype_annotation, test/test_fx.py::TestFX::test_pickle_torch_custom_ops, test/test_fx.py::TestFX::test_prepend_self, test/test_fx.py::TestFX::test_pretty_print, test/test_fx.py::TestFX::test_pretty_print_graph, test/test_fx.py::TestFX::test_pretty_print_node, test/test_fx.py::TestFX::test_pretty_print_targets, test/test_fx.py::TestFX::test_profiler_ranges_side_effect, test/test_fx.py::TestFX::test_proxy_deepcopy_with_tracer, test/test_fx.py::TestFX::test_proxy_deepcopy_without_tracer, test/test_fx.py::TestFX::test_pytree, test/test_fx.py::TestFX::test_pytree_concrete, test/test_fx.py::TestFX::test_reassign_args_kwargs_uses, test/test_fx.py::TestFX::test_regular_and_default_args, test/test_fx.py::TestFX::test_remove_uses, test/test_fx.py::TestFX::test_remove_uses_with_custom_filter, test/test_fx.py::TestFX::test_replace_input, test/test_fx.py::TestFX::test_replace_uses, test/test_fx.py::TestFX::test_reserved_getattr, test/test_fx.py::TestFX::test_return_tuple, test/test_fx.py::TestFX::test_return_type_exists, test/test_fx.py::TestFX::test_return_type_exists_pre_pep585, test/test_fx.py::TestFX::test_script_method_trace, test/test_fx.py::TestFX::test_script_tensor_constant, test/test_fx.py::TestFX::test_sequential, test/test_fx.py::TestFX::test_shape_prop_aggregate, test/test_fx.py::TestFX::test_shape_prop_layout, test/test_fx.py::TestFX::test_shape_prop_layout_3d, test/test_fx.py::TestFX::test_shape_prop_unbacked_sym, test/test_fx.py::TestFX::test_single_default_arg, test/test_fx.py::TestFX::test_snake_case, test/test_fx.py::TestFX::test_sqrt, test/test_fx.py::TestFX::test_stack_traces, test/test_fx.py::TestFX::test_stack_traces_with_transformer, test/test_fx.py::TestFX::test_string_literal_return, test/test_fx.py::TestFX::test_submodule_manipulation_API, test/test_fx.py::TestFX::test_symbolic_trace_assert, test/test_fx.py::TestFX::test_symbolic_trace_sequential, test/test_fx.py::TestFX::test_tensor_attribute, test/test_fx.py::TestFX::test_tensor_attribute_coalseced, test/test_fx.py::TestFX::test_tensor_constant, test/test_fx.py::TestFX::test_throw_out_variant, test/test_fx.py::TestFX::test_torch_custom_ops, test/test_fx.py::TestFX::test_torch_fx_getattr, test/test_fx.py::TestFX::test_torch_fx_len, test/test_fx.py::TestFX::test_torch_op_overloads, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx_tensor_arg, test/test_fx.py::TestFX::test_trace_buffer_slice, test/test_fx.py::TestFX::test_trace_dict_int_keys, test/test_fx.py::TestFX::test_trace_dict_proxy_keys, test/test_fx.py::TestFX::test_trace_fn_constant, test/test_fx.py::TestFX::test_trace_function, test/test_fx.py::TestFX::test_trace_multiple_funcs, test/test_fx.py::TestFX::test_trace_return_dataclass, test/test_fx.py::TestFX::test_trace_return_dataclass_nested, test/test_fx.py::TestFX::test_trace_return_namedtuple, test/test_fx.py::TestFX::test_tracing_graphmodules_as_leaf_submodules, test/test_fx.py::TestFX::test_transformer_multi_outputs, test/test_fx.py::TestFX::test_transformer_noop, test/test_fx.py::TestFX::test_transformer_op_swap, test/test_fx.py::TestFX::test_transformer_preserves_nn_module_stack_for_get_attr, test/test_fx.py::TestFX::test_tuple_no_subscript, test/test_fx.py::TestFX::test_typename_print, test/test_fx.py::TestFX::test_typename_print_pre_pep585, test/test_fx.py::TestFX::test_unpack, test/test_fx.py::TestFX::test_unpack_dict_better_error, test/test_fx.py::TestFX::test_unpack_list_better_error, test/test_fx.py::TestFX::test_update_args_api, test/test_fx.py::TestFX::test_update_args_kwargs_yells_at_you, test/test_fx.py::TestFX::test_update_kwargs_api, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_function, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_module, test/test_fx.py::TestFX::test_varargs_concrete, test/test_fx.py::TestFX::test_wrap, test/test_fx.py::TestFX::test_wrap_decorated_function, test/test_fx.py::TestFX::test_wrap_fn_directly, test/test_fx.py::TestFX::test_wrap_with_submodule, test/test_fx.py::TestFX::test_wrapped_method, test/test_fx.py::TestFX::test_wrapped_retrace, test/test_fx.py::TestFX::test_wrapped_via_decorator, test/test_fx.py::TestFX::test_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_wrong_target_type, test/test_fx.py::TestFX::test_wrong_topo, test/test_fx.py::TestFXAPIBackwardCompatibility::test_adding_side_effect_function, test/test_fx.py::TestFXAPIBackwardCompatibility::test_class_member_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_function_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_preserve_unused_attr_after_unpickle, test/test_fx.py::TestFXAPIBackwardCompatibility::test_public_api_surface, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_affine_grid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_batch_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy_with_logits, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_tbc, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_similarity, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_ctc_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding_bag, test/test_fx.py::TestFunctionalTracing::test_nn_functional_feature_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gaussian_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_glu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_grid_sample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_group_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gumbel_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardswish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hinge_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_huber_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_instance_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_interpolate, test/test_fx.py::TestFunctionalTracing::test_nn_functional_kl_div, test/test_fx.py::TestFunctionalTracing::test_nn_functional_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_layer_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_linear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_local_response_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_log_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_logsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_margin_ranking_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mse_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_head_attention_forward, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_native_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_normalize, test/test_fx.py::TestFunctionalTracing::test_nn_functional_one_hot, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pad, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pairwise_distance, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pdist, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_unshuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_poisson_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_prelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu6, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rms_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_scaled_dot_product_attention, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_silu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_smooth_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmin, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softplus, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_with_distance_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_unfold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_nearest, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_H_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_T_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___getitem___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___radd___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rdiv___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmatmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmod___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rpow___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rsub___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__batch_norm_with_update_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__chunk_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__native_batch_norm_legit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_lengths_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_offsets_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__softmax_backward_data_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_abs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcdiv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_decomposed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_alias_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_all_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_allclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_aminmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_angle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_any_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_arange_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argsort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argwhere_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_partial_views_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_baddbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bernoulli_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bfloat16_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_block_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bool_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_shapes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bucketize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_byte_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cartesian_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cauchy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdouble_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ceil_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cfloat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chalf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_char_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_inverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_max_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_min_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clone_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_column_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_combinations_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_physical_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_constant_pad_nd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_contiguous_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_copysign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_corrcoef_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_count_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cov_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumulative_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_deg2rad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_embed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagflat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diff_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_digamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_floor_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_no_rounding_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_trunc_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_double_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_einsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_permuted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_equal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expm1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eye_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flip_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fliplr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flipud_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gather_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ge_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geometric_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geqrf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gradient_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_half_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_heaviside_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogram_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogramdd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hypot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igammac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_inner_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_int_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isfinite_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isnan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isneginf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isposinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isreal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_item_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_2inputs_2outputs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_return_by_ref_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_unary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kron_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kthvalue_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ldexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_le_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lerp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lgamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cond_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_det_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eig_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvalsh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_householder_product_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_grad_oriented_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_multi_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_subgradients_at_zero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_singular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_slogdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_triangular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svdvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorsolve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vander_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vecdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vector_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log10_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log1p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logcumsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_and_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_not_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_or_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_xor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_long_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_unpack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mH_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mT_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matrix_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_pool2d_with_indices_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_maximum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_list_of_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_variadic_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_minimum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_movedim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_msort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_multinomial_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nan_to_num_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmedian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanquantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nansum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_dropout_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ne_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nextafter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_alpha_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_celu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_channel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_ctc_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_elu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_bag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_glu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_grid_sample_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_group_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardswish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardtanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hinge_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_huber_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_instance_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_area_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bicubic_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_trilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_kl_div_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_leaky_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_local_response_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_logsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mse_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_circular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_constant_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_reflect_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pairwise_distance_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_prelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu6_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rms_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rrelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_selu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_silu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softplus_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softsign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_tanhshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_threshold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_static_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_fro_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_inf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_nuc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_in_place_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_number_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ormqr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_outer_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pca_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pinverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polar_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_4_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_positive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_quantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rad2deg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rand_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ravel_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_real_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reciprocal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_remainder_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_renorm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_interleave_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize_as__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_roll_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rot90_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_neg_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scalar_tensor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_searchsorted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sgn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_short_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_bartlett_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_blackman_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_gaussian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hann_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_kaiser_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_nuttall_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signbit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_mm_reduce_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_sampled_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_airy_ai_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_entr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_erfcx_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_h_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_he_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i0e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_legendre_polynomial_p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_log_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtri_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_spherical_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_xlog1py_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_zeta_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_list_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_square_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_multiple_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_to_size_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_along_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensor_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensordot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_sparse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_topk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapz_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triangular_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tril_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_true_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trunc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unflatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_uniform_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_consecutive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_where_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_xlogy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zero__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_like_cpu_float32, test/test_fx.py::TestVisionTracing::test_torchvision_models_alexnet, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_base, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_tiny, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet121, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet161, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet169, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet201, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_320_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fcos_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_keypointrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssd300_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssdlite320_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b0, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b1, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b2, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b3, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b4, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b5, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b6, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b7, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_l, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_m, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_googlenet, test/test_fx.py::TestVisionTracing::test_torchvision_models_inception_v3, test/test_fx.py::TestVisionTracing::test_torchvision_models_maxvit_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_75, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_3, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_128gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet152, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet18, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet34, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_32x8d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_64x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext50_32x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_lraspp_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x2_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_1, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mc3_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v1_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r2plus1d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r3d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_s3d, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_h_14, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet101_2, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet50_2
2025-03-14T04:24:31.5563392Z 
2025-03-14T04:24:31.5563660Z Running test_cpp_extensions_mtia_backend 1/1 ... [2025-03-14 04:24:31.464383]
2025-03-14T04:24:31.5564464Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:24:31.5565585Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_mtia_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:24:31.464665]
2025-03-14T04:24:35.3841878Z 
2025-03-14T04:24:35.3842911Z test_cpp_extensions_mtia_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_mtia_backend_1.1_bd249f8b5ee1cd92_.log
2025-03-14T04:24:35.3845872Z Running 5 items in this shard: test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_device_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_get_device_module, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_basic, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context_different_device
2025-03-14T04:24:35.3848139Z 
2025-03-14T04:24:35.3848403Z Running inductor/test_max_autotune 1/1 ... [2025-03-14 04:24:35.384318]
2025-03-14T04:24:35.3849038Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:24:35.3850145Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_max_autotune.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:24:35.384585]
2025-03-14T04:24:42.1188486Z 
2025-03-14T04:24:42.1189497Z inductor/test_max_autotune 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_max_autotune_1.1_f8efe83b6f9d8237_.log
2025-03-14T04:24:42.1190274Z 
2025-03-14T04:24:42.1191432Z Running test_cpp_extensions_stream_and_event 1/1 ... [2025-03-14 04:24:42.118985]
2025-03-14T04:24:42.1191949Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:24:42.1194579Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_stream_and_event.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:24:42.119263]
2025-03-14T04:24:45.9885792Z 
2025-03-14T04:24:45.9886828Z test_cpp_extensions_stream_and_event 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_stream_and_event_1.1_cbbcda4faa3fd86b_.log
2025-03-14T04:24:45.9888146Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event
2025-03-14T04:24:45.9889613Z 
2025-03-14T04:24:45.9889837Z Running test_cpp_extensions_jit 1/1 ... [2025-03-14 04:24:45.988732]
2025-03-14T04:24:45.9890300Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:24:45.9892486Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:24:45.989035]
2025-03-14T04:25:18.8971357Z 
2025-03-14T04:25:18.8972321Z test_cpp_extensions_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_jit_1.1_4c81e7ddae209344_.log
2025-03-14T04:25:18.8984290Z Running 29 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_xpu, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_load_with_non_platform_default_encoding, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning
2025-03-14T04:25:18.8995543Z 
2025-03-14T04:25:18.8995738Z Running test_native_mha 1/1 ... [2025-03-14 04:25:18.897320]
2025-03-14T04:25:18.8996159Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:25:18.8997198Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_native_mha.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:25:18.897612]
2025-03-14T04:25:22.4167145Z 
2025-03-14T04:25:22.4168030Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_c30b9118449ba333_.log
2025-03-14T04:25:22.4193043Z Running 28 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_encoder_decoder_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::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, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_nested_cpu_float32
2025-03-14T04:25:22.4214594Z 
2025-03-14T04:25:22.4214833Z Running test_tensor_creation_ops 1/1 ... [2025-03-14 04:25:22.416889]
2025-03-14T04:25:22.4215376Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:25:22.4216459Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensor_creation_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:25:22.417183]
2025-03-14T04:25:44.4105089Z 
2025-03-14T04:25:44.4106146Z test_tensor_creation_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensor_creation_ops_1.1_494dcae6936ad673_.log
2025-03-14T04:25:44.4342231Z Running 640 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, 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test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_roll_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_simple_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_storage_filename_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_strided_mismatched_stride_shape_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_ctor_device_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factories_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_copy_var_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_non_writable_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_sequence_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_out_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_std_error_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_distribution_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_inference_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_random_neg_values_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_device_compatibility_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_large_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_empty_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_full_like_inference_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_byte_to_int_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_astensor_consistency_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_default_device_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_device_without_index_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_numpy_scalars_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_mult_devices_cpu_float32
2025-03-14T04:25:44.4687071Z 
2025-03-14T04:25:44.4687455Z Running test_sort_and_select 1/1 ... [2025-03-14 04:25:44.411459]
2025-03-14T04:25:44.4688245Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:25:44.4690275Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:25:44.411759]
2025-03-14T04:25:49.6828963Z 
2025-03-14T04:25:49.6829885Z test_sort_and_select 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sort_and_select_1.1_8beb94e14859aae0_.log
2025-03-14T04:25:49.6869064Z Running 113 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCPU::test_complex_unsupported_cpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_dtypes_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_scalar_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_slow_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_expanded_tensor_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_4d_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_arguments_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_noncontiguous_gpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_quantized_scalar_input_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_dim_cpu
2025-03-14T04:25:49.6906955Z 
2025-03-14T04:25:49.6907151Z Running test_dispatch 1/1 ... [2025-03-14 04:25:49.683211]
2025-03-14T04:25:49.6907573Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:25:49.6908615Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:25:49.683500]
2025-03-14T04:26:17.3836233Z 
2025-03-14T04:26:17.3837156Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_763b3c15d40b280c_.log
2025-03-14T04:26:17.3847206Z Running 32 items in this shard: test/test_dispatch.py::TestDispatch::test_all_invariants, test/test_dispatch.py::TestDispatch::test_computed_table, test/test_dispatch.py::TestDispatch::test_computed_table_with_ambiguous_autogradother, test/test_dispatch.py::TestDispatch::test_computed_table_with_autograd, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math_autogradcpu_fallthrough, test/test_dispatch.py::TestDispatch::test_computed_table_with_math, test/test_dispatch.py::TestDispatch::test_def, test/test_dispatch.py::TestDispatch::test_def_impl_schema_mismatch, test/test_dispatch.py::TestDispatch::test_def_only, test/test_dispatch.py::TestDispatch::test_def_with_explicit_alias, test/test_dispatch.py::TestDispatch::test_def_with_inference, test/test_dispatch.py::TestDispatch::test_dispatch_print_registrations_for_dispatch_key_invalid, test/test_dispatch.py::TestDispatch::test_find_dangling_impls, test/test_dispatch.py::TestDispatch::test_find_dangling_impls_ext, test/test_dispatch.py::TestDispatch::test_impl_only, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_defaulting, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_mismatch, test/test_dispatch.py::TestDispatch::test_multiple_def_error, test/test_dispatch.py::TestDispatch::test_multiple_fallback, test/test_dispatch.py::TestDispatch::test_overwrite_math, test/test_dispatch.py::TestPythonDispatcher::test_autogradother, test/test_dispatch.py::TestPythonDispatcher::test_basic, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_math, test/test_dispatch.py::TestPythonDispatcher::test_duplicate_registrations, test/test_dispatch.py::TestPythonDispatcher::test_math_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_quantized_structured_not_implemented
2025-03-14T04:26:17.3856882Z 
2025-03-14T04:26:17.3857111Z Running test_cuda_primary_ctx 1/1 ... [2025-03-14 04:26:17.383811]
2025-03-14T04:26:17.3857562Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:26:17.3858688Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_primary_ctx.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:26:17.384125]
2025-03-14T04:26:20.7693114Z 
2025-03-14T04:26:20.7694075Z test_cuda_primary_ctx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_primary_ctx_1.1_6ae877e680d989b0_.log
2025-03-14T04:26:20.7694902Z Running 0 items in this shard:
2025-03-14T04:26:20.7695122Z 
2025-03-14T04:26:20.7696232Z Running test_multiprocessing_spawn 1/1 ... [2025-03-14 04:26:20.769464]
2025-03-14T04:26:20.7696698Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:26:20.7699554Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing_spawn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:26:20.769751]
2025-03-14T04:28:34.8595427Z 
2025-03-14T04:28:34.8596509Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_9cec17e76ba11037_.log
2025-03-14T04:28:34.8608030Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable
2025-03-14T04:28:34.8618752Z 
2025-03-14T04:28:34.8618966Z Running nn/test_convolution 1/1 ... [2025-03-14 04:28:34.859473]
2025-03-14T04:28:34.8619418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:28:34.8620495Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_convolution.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:28:34.859799]
2025-03-14T04:28:57.9577483Z 
2025-03-14T04:28:57.9578427Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_974dea72bba1d7a5_.log
2025-03-14T04:28:57.9914994Z Running 588 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv1d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_1x1, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_backward_twice, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias_v2, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_with_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_without_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_missing_argument, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_wbias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_half_cublas_gemm, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size_downsample_upsample, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose3d_correct_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv1d_issue_120547, test/nn/test_convolution.py::TestConvolutionNN::test_conv2d_discontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_issue_120406, test/nn/test_convolution.py::TestConvolutionNN::test_conv_backcompat, test/nn/test_convolution.py::TestConvolutionNN::test_conv_cudnn_memory_layout_dominance, test/nn/test_convolution.py::TestConvolutionNN::test_conv_invalid_groups, test/nn/test_convolution.py::TestConvolutionNN::test_conv_modules_raise_error_on_incorrect_input_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv_padding_mode, test/nn/test_convolution.py::TestConvolutionNN::test_conv_shapecheck, test/nn/test_convolution.py::TestConvolutionNN::test_conv_tbc, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_non_contiguous, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_noncontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_not_mutate_stride, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv1d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv3d, test/nn/test_convolution.py::TestConvolutionNN::test_mismatch_shape_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_nnpack_conv, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, test/nn/test_convolution.py::TestConvolutionNN::test_thnn_conv_strided_padded_dilated, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose3d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_contig_wrong_stride_cudnn_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_no_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_64bit_indexing_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_False_cpu, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_contiguous_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_mismatch_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_groups_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_no_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_stride_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_strided_with_3D_input_and_weight_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_ic1_channels_last_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_nosplit_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_and_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose2d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose3d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transposed_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv2d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv3d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_conv_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float64
2025-03-14T04:28:58.0242472Z 
2025-03-14T04:28:58.0242672Z Running nn/test_pooling 1/1 ... [2025-03-14 04:28:57.958761]
2025-03-14T04:28:58.0243105Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:28:58.0244179Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_pooling.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:28:57.959062]
2025-03-14T04:29:25.8164809Z 
2025-03-14T04:29:25.8165693Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_c42f255728b10089_.log
2025-03-14T04:29:25.8204927Z Running 104 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool3d, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_with_indices_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu
2025-03-14T04:29:25.8243320Z 
2025-03-14T04:29:25.8243540Z Running test_mobile_optimizer 1/1 ... [2025-03-14 04:29:25.816706]
2025-03-14T04:29:25.8243991Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:29:25.8245060Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:29:25.816983]
2025-03-14T04:29:31.7890367Z 
2025-03-14T04:29:31.7891354Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_50f6ba23af44fadc_.log
2025-03-14T04:29:31.7894454Z Running 7 items in this shard: test/test_mobile_optimizer.py::TestOptimizer::test_clone_module_with_class, test/test_mobile_optimizer.py::TestOptimizer::test_generate_mobile_module_lints, test/test_mobile_optimizer.py::TestOptimizer::test_hoist_conv_packed_params, test/test_mobile_optimizer.py::TestOptimizer::test_mobilenet_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_preserve_bundled_inputs_methods, test/test_mobile_optimizer.py::TestOptimizer::test_quantized_conv_no_asan_failures
2025-03-14T04:29:31.7896936Z 
2025-03-14T04:29:31.7897106Z Running test_nn 1/1 ... [2025-03-14 04:29:31.789243]
2025-03-14T04:29:31.7897505Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:29:31.7898519Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:29:31.789553]
2025-03-14T04:31:47.1288453Z 
2025-03-14T04:31:47.1289398Z test_nn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.1_bd8996e90761f159_.log
2025-03-14T04:31:47.2448590Z Running 2193 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_CTCLoss_critical_target_len, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cpu, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cuda, test/test_nn.py::TestNN::test_CTCLoss_long_targets, test/test_nn.py::TestNN::test_CTCLoss_typechecks, test/test_nn.py::TestNN::test_CTCLoss_zero_infinity, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv2d_padding, test/test_nn.py::TestNN::test_Conv2d_padding_cuda, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_EmbeddingBag_sum_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx_cuda, test/test_nn.py::TestNN::test_Embedding_cuda, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Embedding_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Flatten_no_batch_dim, test/test_nn.py::TestNN::test_Flatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Fold, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_GLU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_HuberLoss_delta, test/test_nn.py::TestNN::test_HuberLoss_delta_cuda, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_size, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear, test/test_nn.py::TestNN::test_Linear_cuda, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MSELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleDict, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_change_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_no_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_with_dropout, test/test_nn.py::TestNN::test_RNN_cudnn_weight_norm, test/test_nn.py::TestNN::test_RNN_dropout, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RNN_input_size_zero, test/test_nn.py::TestNN::test_RNN_nonlinearity, test/test_nn.py::TestNN::test_RNN_nonlinearity_passed_as_arg, test/test_nn.py::TestNN::test_RReLU, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar_cuda, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReLU_no_batch_dim, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_add, test/test_nn.py::TestNN::test_Sequential_append, test/test_nn.py::TestNN::test_Sequential_delitem, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_Sequential_imul, test/test_nn.py::TestNN::test_Sequential_insert, test/test_nn.py::TestNN::test_Sequential_insert_fail_case, test/test_nn.py::TestNN::test_Sequential_mul, test/test_nn.py::TestNN::test_Sequential_pop, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_Sequential_setitem, test/test_nn.py::TestNN::test_Sequential_setitem_named, test/test_nn.py::TestNN::test_SiLU_no_batch_dim, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softsign_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanh_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Threshold_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_Unfold_int_input, test/test_nn.py::TestNN::test_Unfold_int_input_cuda, test/test_nn.py::TestNN::test_adaptive_log_softmax, test/test_nn.py::TestNN::test_add_module, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_3, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_weight_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_loss_always_nonnegative, test/test_nn.py::TestNN::test_bce_loss_broadcasts_weights, test/test_nn.py::TestNN::test_bce_loss_input_range, test/test_nn.py::TestNN::test_bce_loss_size_mismatch, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_pos_weights, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_raises_if_target_and_input_are_different_size, test/test_nn.py::TestNN::test_bce_with_logits_stability, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_bilinear_broadcasting, test/test_nn.py::TestNN::test_bilinear_no_bias, test/test_nn.py::TestNN::test_bilinear_non_contiguous, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_broadcast_not_requiring_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent, test/test_nn.py::TestNN::test_buffer_not_persistent_assign, test/test_nn.py::TestNN::test_buffer_not_persistent_del, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffer_not_persistent_overwrite, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_call_supports_python_dict_output, test/test_nn.py::TestNN::test_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_children, test/test_nn.py::TestNN::test_container_copy, test/test_nn.py::TestNN::test_convert_sync_batchnorm, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_diff_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_nonexpandable_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_invalid_shape, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cosine_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cross_entropy_loss_zero_div, test/test_nn.py::TestNN::test_cudnn_forward_exception, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_format, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_dir, test/test_nn.py::TestNN::test_dir_digit, test/test_nn.py::TestNN::test_elu_inplace_gradgrad, test/test_nn.py::TestNN::test_elu_inplace_on_view, test/test_nn.py::TestNN::test_error_RNN_seq_len_zero, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fold_invalid_arg, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_gaussian_nll_loss_scalar_var, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_get_buffer_from_submodules, test/test_nn.py::TestNN::test_getattr_with_property, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_grid_sample_nearest_neighbor_rounding_mode_consistency, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate, test/test_nn.py::TestNN::test_interpolate_bicubic_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_buffer_overflow, test/test_nn.py::TestNN::test_interpolate_illegal_memory_access, test/test_nn.py::TestNN::test_interpolate_linear_1d, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d, test/test_nn.py::TestNN::test_interpolate_nearest_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_kl_div_log_softmax_target, test/test_nn.py::TestNN::test_kl_div_with_diff_type, test/test_nn.py::TestNN::test_kl_div_with_diff_type_log_target, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_eps, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_layer_norm_large_tensor, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightStrided, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_linear_broadcasting, test/test_nn.py::TestNN::test_linear_raise_on_scalar_input, test/test_nn.py::TestNN::test_log_softmax_dim0, test/test_nn.py::TestNN::test_log_softmax_dim0_cuda, test/test_nn.py::TestNN::test_log_softmax_dim3, test/test_nn.py::TestNN::test_log_softmax_dim3_cuda, test/test_nn.py::TestNN::test_log_softmax_lastdim, test/test_nn.py::TestNN::test_log_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_scalar_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial, test/test_nn.py::TestNN::test_log_softmax_spatial_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial_special, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_margin_ranking_loss_margin_no_reduce, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_max_pool1d_invalid_output_size, test/test_nn.py::TestNN::test_module_apply_inplace_op, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_module_to_argparse, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_mse_loss_size_warning, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_nn.py::TestNN::test_named_children, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_native_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_nested_tensor_from_mask_error, test/test_nn.py::TestNN::test_no_grad, test/test_nn.py::TestNN::test_non_leaf_parameters, test/test_nn.py::TestNN::test_normalize, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_pad_scalar_error, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_pairwise_distance, test/test_nn.py::TestNN::test_parameter_assignment, test/test_nn.py::TestNN::test_parameterlistdict_pickle, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_parameters_and_named_parameters, test/test_nn.py::TestNN::test_parameters_to_vector, test/test_nn.py::TestNN::test_parse_to, test/test_nn.py::TestNN::test_partial_flat_weights, test/test_nn.py::TestNN::test_pdist, test/test_nn.py::TestNN::test_pdist_cpu_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_cuda_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pdist_empty_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_projections_lstm_check_device, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_not_tensor, test/test_nn.py::TestNN::test_register_parameter_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_functional_dim3_cuda, test/test_nn.py::TestNN::test_softmax_functional_scalar, test/test_nn.py::TestNN::test_softmax_functional_scalar_cuda, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_cuda, test/test_nn.py::TestNN::test_softmax_spatial_dtype, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_sync_batchnorm_accuracy_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_backward_elemt, test/test_nn.py::TestNN::test_threshold_bfloat16_half, test/test_nn.py::TestNN::test_threshold_int, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_train_errors_for_invalid_mode, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformer_layer_args_check, test/test_nn.py::TestNN::test_transformerdecoder, test/test_nn.py::TestNN::test_transformerdecoderlayer, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unflatten_invalid_arg, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingLinear1d, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestNN::test_vector_to_parameters, test/test_nn.py::TestNN::test_weight_norm, test/test_nn.py::TestNN::test_weight_norm_pickle, test/test_nn.py::TestNN::test_weighted_huber_loss, test/test_nn.py::TestNN::test_weighted_l1_loss_with_weights, test/test_nn.py::TestNN::test_weighted_mse_loss, test/test_nn.py::TestNN::test_zero_grad, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_BatchNorm_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_Bilinear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_empty_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GRU_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_raises_error_if_one_value_per_group_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LocalResponseNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_deterministic_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_fails_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate45_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate90_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_update_stats_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_channel_shuffle_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_error_if_nonfinite_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_index_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_all_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_bfloat16_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_weight_bias_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_logsigmoid_out_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_lstmcell_backward_only_one_output_grad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_TxT_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_devices_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_mask_types_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_non_recursive_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_all_ignored_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_mean_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_target_dim_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_mismatched_batch_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_replicatepad_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_retain_variables_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_save_lstm_compatibility_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_silu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smoothl1loss_backward_zero_beta_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_smem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_grad_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_without_fully_vectorized_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_double_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_low_threshold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_negative_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_gelu_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_rescale_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32
2025-03-14T04:31:47.3592759Z 
2025-03-14T04:31:47.3593002Z Running test_cuda_nvml_based_avail 1/1 ... [2025-03-14 04:31:47.132237]
2025-03-14T04:31:47.3593486Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:31:47.3594660Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:31:47.132562]
2025-03-14T04:31:50.6303094Z 
2025-03-14T04:31:50.6304022Z test_cuda_nvml_based_avail 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_nvml_based_avail_1.1_62200e79551bf155_.log
2025-03-14T04:31:50.6304858Z Running 0 items in this shard:
2025-03-14T04:31:50.6305128Z 
2025-03-14T04:31:50.6306308Z Running test_cuda_trace 1/1 ... [2025-03-14 04:31:50.630466]
2025-03-14T04:31:50.6306734Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:31:50.6309463Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_trace.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:31:50.630761]
2025-03-14T04:31:53.9811915Z 
2025-03-14T04:31:53.9812765Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_70b4a032ec452df9_.log
2025-03-14T04:31:53.9813518Z Running 0 items in this shard:
2025-03-14T04:31:53.9813773Z 
2025-03-14T04:31:53.9815010Z Running test_spectral_ops 1/1 ... [2025-03-14 04:31:53.981359]
2025-03-14T04:31:53.9815448Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:31:53.9818251Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_spectral_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:31:53.981642]
2025-03-14T04:32:04.6131952Z 
2025-03-14T04:32:04.6133063Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_b8e6a435ed65d984_.log
2025-03-14T04:32:04.6223597Z Running 281 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_ifft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft2_invalid_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_float16, 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test/test_spectral_ops.py::TestFFTCPU::test_stft_window_device_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_hfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ihfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftn_cpu
2025-03-14T04:32:04.6312645Z 
2025-03-14T04:32:04.6312989Z Running distributions/test_distributions 1/1 ... [2025-03-14 04:32:04.613669]
2025-03-14T04:32:04.6313502Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:32:04.6314668Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:32:04.613947]
2025-03-14T04:32:59.5524143Z 
2025-03-14T04:32:59.5525358Z distributions/test_distributions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.1_51d6cfdae35605d0_.log
2025-03-14T04:32:59.5613796Z Running 226 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_distribution_expand, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_log_prob, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multinomial_sequential_draw, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, test/distributions/test_distributions.py::TestDistributions::test_wishart_moments, test/distributions/test_distributions.py::TestDistributions::test_wishart_properties, test/distributions/test_distributions.py::TestDistributions::test_wishart_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_shape, test/distributions/test_distributions.py::TestDistributions::test_wishart_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_zero_excluded_binomial, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_alpha, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_beta, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape_vectorized_n, test/distributions/test_distributions.py::TestDistributionShapes::test_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_sample, test/distributions/test_distributions.py::TestJit::test_variance
2025-03-14T04:32:59.5700908Z 
2025-03-14T04:32:59.5701145Z Running test_autoload_disable 1/1 ... [2025-03-14 04:32:59.552987]
2025-03-14T04:33:02.1785676Z running install
2025-03-14T04:33:02.1786795Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:33:02.1787603Z !!
2025-03-14T04:33:02.1787758Z 
2025-03-14T04:33:02.1787894Z         ********************************************************************************
2025-03-14T04:33:02.1788312Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:33:02.1788765Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:33:02.1789157Z         standards-based tools.
2025-03-14T04:33:02.1789366Z 
2025-03-14T04:33:02.1789688Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:33:02.1790242Z         ********************************************************************************
2025-03-14T04:33:02.1790507Z 
2025-03-14T04:33:02.1790599Z !!
2025-03-14T04:33:02.1790846Z   self.initialize_options()
2025-03-14T04:33:02.1919035Z running build
2025-03-14T04:33:02.1919359Z running build_py
2025-03-14T04:33:02.1997187Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:33:02.1999201Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:33:02.2002559Z running build_ext
2025-03-14T04:33:02.2811176Z building 'torch_test_cpp_extension.cpp' extension
2025-03-14T04:33:02.2812019Z creating build/temp.linux-x86_64-cpython-313
2025-03-14T04:33:02.2816876Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/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
2025-03-14T04:33:03.4829509Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2025-03-14T04:33:03.4830817Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2025-03-14T04:33:03.4832505Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9,
2025-03-14T04:33:03.4833414Z                  from extension.cpp:1:
2025-03-14T04:33:03.4839766Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2025-03-14T04:33:03.4840821Z extension.cpp:45:53:   required from here
2025-03-14T04:33:03.4842467Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2025-03-14T04:33:03.4843716Z  1539 | class class_ : public detail::generic_type {
2025-03-14T04:33:03.4844082Z       |       ^~~~~~
2025-03-14T04:33:03.4845699Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2025-03-14T04:33:03.4847214Z extension.cpp:45:53:   required from here
2025-03-14T04:33:03.4850649Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2025-03-14T04:33:03.4853309Z  1599 |             with_internals([&](internals &internals) {
2025-03-14T04:33:03.4853718Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:03.4854254Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2025-03-14T04:33:03.4854850Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:03.4855443Z  1601 |                                                       : internals.registered_types_cpp;
2025-03-14T04:33:03.4855878Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:03.4856319Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2025-03-14T04:33:03.4856745Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:03.4857158Z  1603 |                     = instances[std::type_index(typeid(type))];
2025-03-14T04:33:03.4857570Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:03.4857917Z  1604 |             });
2025-03-14T04:33:03.4858189Z       |             ~               
2025-03-14T04:33:03.4861359Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:03.9054878Z building 'torch_test_cpp_extension.maia' extension
2025-03-14T04:33:03.9059658Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:33:05.2288018Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:05.6442027Z building 'torch_test_cpp_extension.rng' extension
2025-03-14T04:33:05.6446107Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/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
2025-03-14T04:33:07.1010309Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:07.1011504Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:07.1012317Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:07.1013242Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:07.1013955Z                  from rng_extension.cpp:6:
2025-03-14T04:33:07.1014783Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1158: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:07.1015601Z  1158 | # pragma unroll
2025-03-14T04:33:07.1015872Z       | 
2025-03-14T04:33:07.1016426Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1198,
2025-03-14T04:33:07.1017338Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:07.1018152Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:07.1019039Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:07.1020074Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:07.1020856Z                  from rng_extension.cpp:6:
2025-03-14T04:33:07.1021648Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:07.1022435Z    59 | #pragma unroll
2025-03-14T04:33:07.1022709Z       | 
2025-03-14T04:33:07.1023384Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:07.1024175Z    72 | #pragma unroll
2025-03-14T04:33:07.1024445Z       | 
2025-03-14T04:33:07.1025137Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:07.1025908Z    87 | #pragma unroll
2025-03-14T04:33:07.1026173Z       | 
2025-03-14T04:33:07.1026726Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1199,
2025-03-14T04:33:07.1027639Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:07.1028459Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:07.1029315Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:07.1030240Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:07.1030931Z                  from rng_extension.cpp:6:
2025-03-14T04:33:07.1031749Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:07.1032548Z   153 | #pragma unroll
2025-03-14T04:33:07.1032819Z       | 
2025-03-14T04:33:07.1035993Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:07.5263566Z running install_lib
2025-03-14T04:33:07.5343468Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:07.5441444Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:07.5537992Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:07.5642724Z running install_egg_info
2025-03-14T04:33:07.5817958Z running egg_info
2025-03-14T04:33:07.5887200Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-14T04:33:07.5890361Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-14T04:33:07.5892099Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-14T04:33:07.5893912Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-14T04:33:07.5968823Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:33:07.5976850Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:33:07.5978333Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-14T04:33:07.5979708Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-14T04:33:07.5985509Z running install_scripts
2025-03-14T04:33:10.9456773Z 
2025-03-14T04:33:10.9457014Z Running tests...
2025-03-14T04:33:10.9457370Z ----------------------------------------------------------------------
2025-03-14T04:33:10.9584677Z .
2025-03-14T04:33:10.9584988Z ----------------------------------------------------------------------
2025-03-14T04:33:10.9585388Z Ran 1 test in 0.013s
2025-03-14T04:33:10.9585553Z 
2025-03-14T04:33:10.9585657Z OK
2025-03-14T04:33:10.9585777Z 
2025-03-14T04:33:10.9585910Z Generating XML reports...
2025-03-14T04:33:11.6566797Z Running doctests 1/1 ... [2025-03-14 04:33:11.656383]
2025-03-14T04:33:11.7446857Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch')
2025-03-14T04:33:11.7447392Z Listing tests
2025-03-14T04:33:11.9450722Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1507.
2025-03-14T04:33:11.9451623Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:11.9452007Z 
2025-03-14T04:33:11.9452192Z dim_order(ambiguity_check=False) -> tuple
2025-03-14T04:33:11.9452429Z 
2025-03-14T04:33:11.9452681Z Returns the uniquely determined tuple of int describing the dim order or
2025-03-14T04:33:11.9453150Z physical layout of :attr:`self`.
2025-03-14T04:33:11.9453370Z 
2025-03-14T04:33:11.9453631Z The dim order represents how dimensions are laid out in memory of dense tensors,
2025-03-14T04:33:11.9454199Z starting from the outermost to the innermost dimension.
2025-03-14T04:33:11.9454501Z 
2025-03-14T04:33:11.9454693Z Note that the dim order may not always be uniquely determined.
2025-03-14T04:33:11.9455416Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined;
2025-03-14T04:33:11.9456339Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted
2025-03-14T04:33:11.9457116Z into exactly one of the given memory formats, or it cannot be uniquely determined.
2025-03-14T04:33:11.9458047Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness.
2025-03-14T04:33:11.9458625Z Otherwise, it will raise TypeError.
2025-03-14T04:33:11.9458853Z 
2025-03-14T04:33:11.9458943Z Args:
2025-03-14T04:33:11.9459400Z     ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order.
2025-03-14T04:33:11.9459851Z 
2025-03-14T04:33:11.9459982Z Examples::
2025-03-14T04:33:11.9460127Z 
2025-03-14T04:33:11.9460247Z     >>> torch.empty((2, 3, 5, 7)).dim_order()
2025-03-14T04:33:11.9460579Z     (0, 1, 2, 3)
2025-03-14T04:33:11.9460897Z     >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order()
2025-03-14T04:33:11.9461277Z     (0, 2, 1, 3)
2025-03-14T04:33:11.9461639Z     >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order()
2025-03-14T04:33:11.9462067Z     (0, 2, 3, 1)
2025-03-14T04:33:11.9462335Z     >>> torch.empty((1, 2, 3, 4)).dim_order()
2025-03-14T04:33:11.9462662Z     (0, 1, 2, 3)
2025-03-14T04:33:11.9462907Z     >>> try:
2025-03-14T04:33:11.9463226Z     ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True)
2025-03-14T04:33:11.9463637Z     ... except RuntimeError as e:
2025-03-14T04:33:11.9463946Z     ...     print(e)
2025-03-14T04:33:11.9464408Z     The tensor does not have unique dim order, or cannot map to exact one of the given memory formats.
2025-03-14T04:33:11.9464961Z     >>> torch.empty((1, 2, 3, 4)).dim_order(
2025-03-14T04:33:11.9465408Z     ...     ambiguity_check=[torch.contiguous_format, torch.channels_last]
2025-03-14T04:33:11.9465952Z     ... )  # It can be mapped to contiguous format
2025-03-14T04:33:11.9466298Z     (0, 1, 2, 3)
2025-03-14T04:33:11.9466544Z     >>> try:
2025-03-14T04:33:11.9466878Z     ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL")
2025-03-14T04:33:11.9467302Z     ... except TypeError as e:
2025-03-14T04:33:11.9467601Z     ...     print(e)
2025-03-14T04:33:11.9468022Z     The ambiguity_check argument must be a bool or a list of memory formats.
2025-03-14T04:33:11.9468393Z 
2025-03-14T04:33:11.9468495Z .. warning::
2025-03-14T04:33:11.9468843Z     The dim_order tensor API is experimental and subject to change.
2025-03-14T04:33:11.9469156Z 
2025-03-14T04:33:11.9469441Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:11.9469810Z 
2025-03-14T04:33:11.9983545Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=446.
2025-03-14T04:33:11.9984435Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:11.9985094Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2025-03-14T04:33:11.9985587Z 
2025-03-14T04:33:11.9985774Z         This is helpful when you want to visualize data over some
2025-03-14T04:33:11.9986250Z         range of inputs. See below for a plotting example.
2025-03-14T04:33:11.9986538Z 
2025-03-14T04:33:11.9986734Z         Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2025-03-14T04:33:11.9987222Z         inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2025-03-14T04:33:11.9987735Z         this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2025-03-14T04:33:11.9988223Z         G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2025-03-14T04:33:11.9988704Z         the output :math:`G_i` is constructed by expanding :math:`T_i`
2025-03-14T04:33:11.9989136Z         to the result shape.
2025-03-14T04:33:11.9989326Z 
2025-03-14T04:33:11.9989465Z         .. note::
2025-03-14T04:33:11.9989791Z             0D inputs are treated equivalently to 1D inputs of a
2025-03-14T04:33:11.9990186Z             single element.
2025-03-14T04:33:11.9990367Z 
2025-03-14T04:33:11.9990479Z         .. warning::
2025-03-14T04:33:11.9990837Z             `torch.meshgrid(*tensors)` currently has the same behavior
2025-03-14T04:33:11.9991317Z             as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2025-03-14T04:33:11.9991593Z 
2025-03-14T04:33:11.9991865Z             In the future `torch.meshgrid` will transition to
2025-03-14T04:33:11.9992264Z             `indexing='xy'` as the default.
2025-03-14T04:33:11.9992490Z 
2025-03-14T04:33:11.9992709Z             https://github.com/pytorch/pytorch/issues/50276 tracks
2025-03-14T04:33:11.9993202Z             this issue with the goal of migrating to NumPy's behavior.
2025-03-14T04:33:11.9993496Z 
2025-03-14T04:33:11.9993609Z         .. seealso::
2025-03-14T04:33:11.9993763Z 
2025-03-14T04:33:11.9993958Z             :func:`torch.cartesian_prod` has the same effect but it
2025-03-14T04:33:11.9994385Z             collects the data in a tensor of vectors.
2025-03-14T04:33:11.9994643Z 
2025-03-14T04:33:11.9994735Z         Args:
2025-03-14T04:33:11.9995145Z             tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2025-03-14T04:33:11.9995706Z                 treated as tensors of size :math:`(1,)` automatically
2025-03-14T04:33:11.9996000Z 
2025-03-14T04:33:11.9996186Z             indexing: (str, optional): the indexing mode, either "xy"
2025-03-14T04:33:11.9996667Z                 or "ij", defaults to "ij". See warning for future changes.
2025-03-14T04:33:11.9996970Z 
2025-03-14T04:33:11.9997130Z                 If "xy" is selected, the first dimension corresponds
2025-03-14T04:33:11.9997582Z                 to the cardinality of the second input and the second
2025-03-14T04:33:11.9998053Z                 dimension corresponds to the cardinality of the first
2025-03-14T04:33:11.9998525Z                 input.
2025-03-14T04:33:11.9998701Z 
2025-03-14T04:33:11.9998859Z                 If "ij" is selected, the dimensions are in the same
2025-03-14T04:33:11.9999368Z                 order as the cardinality of the inputs.
2025-03-14T04:33:11.9999630Z 
2025-03-14T04:33:11.9999724Z         Returns:
2025-03-14T04:33:12.0000052Z             seq (sequence of Tensors): If the input has :math:`N`
2025-03-14T04:33:12.0000506Z             tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2025-03-14T04:33:12.0001063Z             output will also have :math:`N` tensors, where each tensor
2025-03-14T04:33:12.0001683Z             is of shape :math:`(S_0, ..., S_{N-1})`.
2025-03-14T04:33:12.0002001Z 
2025-03-14T04:33:12.0002116Z         Example::
2025-03-14T04:33:12.0002283Z 
2025-03-14T04:33:12.0002401Z             >>> x = torch.tensor([1, 2, 3])
2025-03-14T04:33:12.0002747Z             >>> y = torch.tensor([4, 5, 6])
2025-03-14T04:33:12.0002982Z 
2025-03-14T04:33:12.0003179Z             Observe the element-wise pairings across the grid, (1, 4),
2025-03-14T04:33:12.0003634Z             (1, 5), ..., (3, 6). This is the same thing as the
2025-03-14T04:33:12.0004067Z             cartesian product.
2025-03-14T04:33:12.0004443Z             >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2025-03-14T04:33:12.0004831Z             >>> grid_x
2025-03-14T04:33:12.0005109Z             tensor([[1, 1, 1],
2025-03-14T04:33:12.0005402Z                     [2, 2, 2],
2025-03-14T04:33:12.0005692Z                     [3, 3, 3]])
2025-03-14T04:33:12.0005976Z             >>> grid_y
2025-03-14T04:33:12.0006244Z             tensor([[4, 5, 6],
2025-03-14T04:33:12.0006518Z                     [4, 5, 6],
2025-03-14T04:33:12.0006802Z                     [4, 5, 6]])
2025-03-14T04:33:12.0006998Z 
2025-03-14T04:33:12.0007169Z             This correspondence can be seen when these grids are
2025-03-14T04:33:12.0007567Z             stacked properly.
2025-03-14T04:33:12.0007971Z             >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2025-03-14T04:33:12.0008435Z             ...             torch.cartesian_prod(x, y))
2025-03-14T04:33:12.0008778Z             True
2025-03-14T04:33:12.0008936Z 
2025-03-14T04:33:12.0009119Z             `torch.meshgrid` is commonly used to produce a grid for
2025-03-14T04:33:12.0009515Z             plotting.
2025-03-14T04:33:12.0009819Z             >>> # xdoctest: +REQUIRES(module:matplotlib)
2025-03-14T04:33:12.0010205Z             >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2025-03-14T04:33:12.0010660Z             >>> import matplotlib.pyplot as plt
2025-03-14T04:33:12.0011047Z             >>> xs = torch.linspace(-5, 5, steps=100)
2025-03-14T04:33:12.0011424Z             >>> ys = torch.linspace(-5, 5, steps=100)
2025-03-14T04:33:12.0011812Z             >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2025-03-14T04:33:12.0012211Z             >>> z = torch.sin(torch.sqrt(x * x + y * y))
2025-03-14T04:33:12.0012591Z             >>> ax = plt.axes(projection='3d')
2025-03-14T04:33:12.0012991Z             >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2025-03-14T04:33:12.0013369Z             >>> plt.show()
2025-03-14T04:33:12.0013545Z 
2025-03-14T04:33:12.0013696Z         .. image:: ../_static/img/meshgrid.png
2025-03-14T04:33:12.0014040Z             :width: 512
2025-03-14T04:33:12.0014201Z 
2025-03-14T04:33:12.0014311Z         
2025-03-14T04:33:12.0014690Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0015074Z 
2025-03-14T04:33:12.0015616Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=842.
2025-03-14T04:33:12.0016485Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0017253Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
2025-03-14T04:33:12.0017760Z 
2025-03-14T04:33:12.0017917Z     Returns the unique elements of the input tensor.
2025-03-14T04:33:12.0020691Z 
2025-03-14T04:33:12.0021030Z     .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2025-03-14T04:33:12.0021679Z         this function also eliminates non-consecutive duplicate values.
2025-03-14T04:33:12.0022007Z 
2025-03-14T04:33:12.0022259Z     .. note:: Currently in the CUDA implementation and the CPU implementation,
2025-03-14T04:33:12.0022915Z         `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2025-03-14T04:33:12.0023629Z         Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2025-03-14T04:33:12.0024227Z         :func:`torch.unique_consecutive` which avoids the sorting.
2025-03-14T04:33:12.0024522Z 
2025-03-14T04:33:12.0024630Z     Args:
2025-03-14T04:33:12.0024895Z         input (Tensor): the input tensor
2025-03-14T04:33:12.0025355Z         sorted (bool): Whether to sort the unique elements in ascending order
2025-03-14T04:33:12.0025857Z             before returning as output.
2025-03-14T04:33:12.0026308Z         return_inverse (bool): Whether to also return the indices for where
2025-03-14T04:33:12.0026880Z             elements in the original input ended up in the returned unique list.
2025-03-14T04:33:12.0027505Z         return_counts (bool): Whether to also return the counts for each unique
2025-03-14T04:33:12.0027944Z             element.
2025-03-14T04:33:12.0028328Z         dim (int, optional): the dimension to operate upon. If ``None``, the
2025-03-14T04:33:12.0028882Z             unique of the flattened input is returned. Otherwise, each of the
2025-03-14T04:33:12.0029433Z             tensors indexed by the given dimension is treated as one of the
2025-03-14T04:33:12.0029983Z             elements to apply the unique operation upon. See examples for more
2025-03-14T04:33:12.0030443Z             details. Default: ``None``
2025-03-14T04:33:12.0030669Z 
2025-03-14T04:33:12.0030766Z     Returns:
2025-03-14T04:33:12.0031205Z         (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2025-03-14T04:33:12.0031626Z 
2025-03-14T04:33:12.0031829Z             - **output** (*Tensor*): the output list of unique scalar elements.
2025-03-14T04:33:12.0032300Z             - **inverse_indices** (*Tensor*): (optional) if
2025-03-14T04:33:12.0032754Z               :attr:`return_inverse` is True, there will be an additional
2025-03-14T04:33:12.0033275Z               returned tensor (same shape as input) representing the indices
2025-03-14T04:33:12.0033871Z               for where elements in the original input map to in the output;
2025-03-14T04:33:12.0034385Z               otherwise, this function will only return a single tensor.
2025-03-14T04:33:12.0034830Z             - **counts** (*Tensor*): (optional) if
2025-03-14T04:33:12.0035255Z               :attr:`return_counts` is True, there will be an additional
2025-03-14T04:33:12.0035749Z               returned tensor (same shape as output or output.size(dim),
2025-03-14T04:33:12.0036261Z               if dim was specified) representing the number of occurrences
2025-03-14T04:33:12.0036697Z               for each unique value or tensor.
2025-03-14T04:33:12.0036929Z 
2025-03-14T04:33:12.0037042Z     Example::
2025-03-14T04:33:12.0037180Z 
2025-03-14T04:33:12.0037413Z         >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2025-03-14T04:33:12.0037848Z         >>> output
2025-03-14T04:33:12.0038106Z         tensor([1, 2, 3])
2025-03-14T04:33:12.0038281Z 
2025-03-14T04:33:12.0038435Z         >>> output, inverse_indices = torch.unique(
2025-03-14T04:33:12.0038930Z         ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-14T04:33:12.0039484Z         >>> output
2025-03-14T04:33:12.0039743Z         tensor([1, 2, 3])
2025-03-14T04:33:12.0040013Z         >>> inverse_indices
2025-03-14T04:33:12.0040305Z         tensor([0, 2, 1, 2])
2025-03-14T04:33:12.0040500Z 
2025-03-14T04:33:12.0040635Z         >>> output, inverse_indices = torch.unique(
2025-03-14T04:33:12.0041238Z         ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-14T04:33:12.0041700Z         >>> output
2025-03-14T04:33:12.0041960Z         tensor([1, 2, 3])
2025-03-14T04:33:12.0042250Z         >>> inverse_indices
2025-03-14T04:33:12.0042543Z         tensor([[0, 2],
2025-03-14T04:33:12.0042815Z                 [1, 2]])
2025-03-14T04:33:12.0042994Z 
2025-03-14T04:33:12.0043100Z         >>> a = torch.tensor([
2025-03-14T04:33:12.0043388Z         ...     [
2025-03-14T04:33:12.0043639Z         ...         [1, 1, 0, 0],
2025-03-14T04:33:12.0043935Z         ...         [1, 1, 0, 0],
2025-03-14T04:33:12.0044230Z         ...         [0, 0, 1, 1],
2025-03-14T04:33:12.0044517Z         ...     ],
2025-03-14T04:33:12.0044760Z         ...     [
2025-03-14T04:33:12.0045008Z         ...         [0, 0, 1, 1],
2025-03-14T04:33:12.0045286Z         ...         [0, 0, 1, 1],
2025-03-14T04:33:12.0045578Z         ...         [1, 1, 1, 1],
2025-03-14T04:33:12.0045868Z         ...     ],
2025-03-14T04:33:12.0046110Z         ...     [
2025-03-14T04:33:12.0046358Z         ...         [1, 1, 0, 0],
2025-03-14T04:33:12.0046684Z         ...         [1, 1, 0, 0],
2025-03-14T04:33:12.0046975Z         ...         [0, 0, 1, 1],
2025-03-14T04:33:12.0047265Z         ...     ],
2025-03-14T04:33:12.0047510Z         ... ])
2025-03-14T04:33:12.0047646Z 
2025-03-14T04:33:12.0047880Z         >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2025-03-14T04:33:12.0048431Z         >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2025-03-14T04:33:12.0049095Z         >>> # each other, so one of them will be removed.
2025-03-14T04:33:12.0049465Z         >>> (a[0, :, :] == a[2, :, :]).all()
2025-03-14T04:33:12.0049789Z         tensor(True)
2025-03-14T04:33:12.0050086Z         >>> a_unique_dim0 = torch.unique(a, dim=0)
2025-03-14T04:33:12.0050437Z         >>> a_unique_dim0
2025-03-14T04:33:12.0050718Z         tensor([[[0, 0, 1, 1],
2025-03-14T04:33:12.0050999Z                  [0, 0, 1, 1],
2025-03-14T04:33:12.0051285Z                  [1, 1, 1, 1]],
2025-03-14T04:33:12.0051570Z                 [[1, 1, 0, 0],
2025-03-14T04:33:12.0051856Z                  [1, 1, 0, 0],
2025-03-14T04:33:12.0052143Z                  [0, 0, 1, 1]]])
2025-03-14T04:33:12.0052344Z 
2025-03-14T04:33:12.0052565Z         >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2025-03-14T04:33:12.0053013Z         >>> # `a_unique_dim0`:
2025-03-14T04:33:12.0053403Z         >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2025-03-14T04:33:12.0053748Z         tensor(True)
2025-03-14T04:33:12.0054041Z         >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2025-03-14T04:33:12.0054385Z         tensor(True)
2025-03-14T04:33:12.0054540Z 
2025-03-14T04:33:12.0054763Z         >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2025-03-14T04:33:12.0055289Z         >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2025-03-14T04:33:12.0055721Z         >>> # them will be removed.
2025-03-14T04:33:12.0056057Z         >>> (a[:, 0, :] == a[:, 1, :]).all()
2025-03-14T04:33:12.0056384Z         tensor(True)
2025-03-14T04:33:12.0056663Z         >>> torch.unique(a, dim=1)
2025-03-14T04:33:12.0056984Z         tensor([[[0, 0, 1, 1],
2025-03-14T04:33:12.0057279Z                  [1, 1, 0, 0]],
2025-03-14T04:33:12.0057558Z                 [[1, 1, 1, 1],
2025-03-14T04:33:12.0057848Z                  [0, 0, 1, 1]],
2025-03-14T04:33:12.0058144Z                 [[0, 0, 1, 1],
2025-03-14T04:33:12.0058441Z                  [1, 1, 0, 0]]])
2025-03-14T04:33:12.0058643Z 
2025-03-14T04:33:12.0058861Z         >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2025-03-14T04:33:12.0059372Z         >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2025-03-14T04:33:12.0059848Z         >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2025-03-14T04:33:12.0060384Z         >>> # sub-tensors will be removed.
2025-03-14T04:33:12.0060730Z         >>> (a[:, :, 0] == a[:, :, 1]).all()
2025-03-14T04:33:12.0061056Z         tensor(True)
2025-03-14T04:33:12.0061339Z         >>> (a[:, :, 2] == a[:, :, 3]).all()
2025-03-14T04:33:12.0061665Z         tensor(True)
2025-03-14T04:33:12.0061945Z         >>> torch.unique(a, dim=2)
2025-03-14T04:33:12.0062263Z         tensor([[[0, 1],
2025-03-14T04:33:12.0062542Z                  [0, 1],
2025-03-14T04:33:12.0062815Z                  [1, 0]],
2025-03-14T04:33:12.0063086Z                 [[1, 0],
2025-03-14T04:33:12.0063351Z                  [1, 0],
2025-03-14T04:33:12.0063604Z                  [1, 1]],
2025-03-14T04:33:12.0063870Z                 [[0, 1],
2025-03-14T04:33:12.0064137Z                  [0, 1],
2025-03-14T04:33:12.0064402Z                  [1, 0]]])
2025-03-14T04:33:12.0064667Z     
2025-03-14T04:33:12.0065051Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0065434Z 
2025-03-14T04:33:12.0168255Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560.
2025-03-14T04:33:12.0169175Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0169569Z 
2025-03-14T04:33:12.0169760Z Load a model from a github repo or a local directory.
2025-03-14T04:33:12.0170046Z 
2025-03-14T04:33:12.0170278Z Note: Loading a model is the typical use case, but this can also be used to
2025-03-14T04:33:12.0170855Z for loading other objects such as tokenizers, loss functions, etc.
2025-03-14T04:33:12.0171196Z 
2025-03-14T04:33:12.0171428Z If ``source`` is 'github', ``repo_or_dir`` is expected to be
2025-03-14T04:33:12.0172034Z of the form ``repo_owner/repo_name[:ref]`` with an optional
2025-03-14T04:33:12.0172478Z ref (a tag or a branch).
2025-03-14T04:33:12.0172665Z 
2025-03-14T04:33:12.0172838Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2025-03-14T04:33:12.0173244Z path to a local directory.
2025-03-14T04:33:12.0173449Z 
2025-03-14T04:33:12.0173539Z Args:
2025-03-14T04:33:12.0173811Z     repo_or_dir (str): If ``source`` is 'github',
2025-03-14T04:33:12.0174359Z         this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2025-03-14T04:33:12.0175078Z         an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2025-03-14T04:33:12.0175997Z         the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2025-03-14T04:33:12.0176664Z         If ``source`` is 'local'  then it should be a path to a local directory.
2025-03-14T04:33:12.0177198Z     model (str): the name of a callable (entrypoint) defined in the
2025-03-14T04:33:12.0177636Z         repo/dir's ``hubconf.py``.
2025-03-14T04:33:12.0178065Z     *args (optional): the corresponding args for callable ``model``.
2025-03-14T04:33:12.0178577Z     source (str, optional): 'github' or 'local'. Specifies how
2025-03-14T04:33:12.0179056Z         ``repo_or_dir`` is to be interpreted. Default is 'github'.
2025-03-14T04:33:12.0179569Z     trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2025-03-14T04:33:12.0180162Z         This parameter was introduced in v1.12 and helps ensuring that users
2025-03-14T04:33:12.0180875Z         only run code from repos that they trust.
2025-03-14T04:33:12.0181170Z 
2025-03-14T04:33:12.0181385Z         - If ``False``, a prompt will ask the user whether the repo should
2025-03-14T04:33:12.0181806Z           be trusted.
2025-03-14T04:33:12.0182171Z         - If ``True``, the repo will be added to the trusted list and loaded
2025-03-14T04:33:12.0182628Z           without requiring explicit confirmation.
2025-03-14T04:33:12.0183058Z         - If ``"check"``, the repo will be checked against the list of
2025-03-14T04:33:12.0183566Z           trusted repos in the cache. If it is not present in that list, the
2025-03-14T04:33:12.0184104Z           behaviour will fall back onto the ``trust_repo=False`` option.
2025-03-14T04:33:12.0184727Z         - If ``None``: this will raise a warning, inviting the user to set
2025-03-14T04:33:12.0185230Z           ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2025-03-14T04:33:12.0185758Z           is only present for backward compatibility and will be removed in
2025-03-14T04:33:12.0186184Z           v2.0.
2025-03-14T04:33:12.0186340Z 
2025-03-14T04:33:12.0186556Z         Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2025-03-14T04:33:12.0187106Z     force_reload (bool, optional): whether to force a fresh download of
2025-03-14T04:33:12.0187635Z         the github repo unconditionally. Does not have any effect if
2025-03-14T04:33:12.0188080Z         ``source = 'local'``. Default is ``False``.
2025-03-14T04:33:12.0188539Z     verbose (bool, optional): If ``False``, mute messages about hitting
2025-03-14T04:33:12.0189080Z         local caches. Note that the message about first download cannot be
2025-03-14T04:33:12.0189596Z         muted. Does not have any effect if ``source = 'local'``.
2025-03-14T04:33:12.0189995Z         Default is ``True``.
2025-03-14T04:33:12.0190487Z     skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2025-03-14T04:33:12.0191231Z         specified by the ``github`` argument properly belongs to the repo owner. This will make
2025-03-14T04:33:12.0191912Z         requests to the GitHub API; you can specify a non-default GitHub token by setting the
2025-03-14T04:33:12.0192505Z         ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2025-03-14T04:33:12.0193037Z     **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-14T04:33:12.0193372Z 
2025-03-14T04:33:12.0193482Z Returns:
2025-03-14T04:33:12.0193811Z     The output of the ``model`` callable when called with the given
2025-03-14T04:33:12.0194226Z     ``*args`` and ``**kwargs``.
2025-03-14T04:33:12.0194415Z 
2025-03-14T04:33:12.0194523Z Example:
2025-03-14T04:33:12.0194803Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-14T04:33:12.0195166Z     >>> # from a github repo
2025-03-14T04:33:12.0195472Z     >>> repo = "pytorch/vision"
2025-03-14T04:33:12.0195787Z     >>> model = torch.hub.load(
2025-03-14T04:33:12.0196168Z     ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2025-03-14T04:33:12.0196576Z     ... )
2025-03-14T04:33:12.0196823Z     >>> # from a local directory
2025-03-14T04:33:12.0197199Z     >>> path = "/some/local/path/pytorch/vision"
2025-03-14T04:33:12.0197562Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:12.0197999Z     >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2025-03-14T04:33:12.0198380Z 
2025-03-14T04:33:12.0198637Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0199021Z 
2025-03-14T04:33:12.0199585Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=652.
2025-03-14T04:33:12.0200426Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0200807Z 
2025-03-14T04:33:12.0201007Z Load a model from a local directory with a ``hubconf.py``.
2025-03-14T04:33:12.0201294Z 
2025-03-14T04:33:12.0201386Z Args:
2025-03-14T04:33:12.0201718Z     hubconf_dir (str): path to a local directory that contains a
2025-03-14T04:33:12.0202139Z         ``hubconf.py``.
2025-03-14T04:33:12.0202509Z     model (str): name of an entrypoint defined in the directory's
2025-03-14T04:33:12.0202926Z         ``hubconf.py``.
2025-03-14T04:33:12.0203313Z     *args (optional): the corresponding args for callable ``model``.
2025-03-14T04:33:12.0203857Z     **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-14T04:33:12.0204203Z 
2025-03-14T04:33:12.0204296Z Returns:
2025-03-14T04:33:12.0204606Z     a single model with corresponding pretrained weights.
2025-03-14T04:33:12.0204902Z 
2025-03-14T04:33:12.0204998Z Example:
2025-03-14T04:33:12.0205336Z     >>> # xdoctest: +SKIP("stub local path")
2025-03-14T04:33:12.0205715Z     >>> path = "/some/local/path/pytorch/vision"
2025-03-14T04:33:12.0206071Z     >>> model = _load_local(
2025-03-14T04:33:12.0206358Z     ...     path,
2025-03-14T04:33:12.0206611Z     ...     "resnet50",
2025-03-14T04:33:12.0206930Z     ...     weights="ResNet50_Weights.IMAGENET1K_V1",
2025-03-14T04:33:12.0207286Z     ... )
2025-03-14T04:33:12.0207411Z 
2025-03-14T04:33:12.0207682Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0208051Z 
2025-03-14T04:33:12.0208547Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=691.
2025-03-14T04:33:12.0209414Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0209955Z Download object at the given URL to a local path.
2025-03-14T04:33:12.0210214Z 
2025-03-14T04:33:12.0210321Z     Args:
2025-03-14T04:33:12.0210590Z         url (str): URL of the object to download
2025-03-14T04:33:12.0211062Z         dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2025-03-14T04:33:12.0211814Z         hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2025-03-14T04:33:12.0212379Z             Default: None
2025-03-14T04:33:12.0212816Z         progress (bool, optional): whether or not to display a progress bar to stderr
2025-03-14T04:33:12.0213306Z             Default: True
2025-03-14T04:33:12.0213493Z 
2025-03-14T04:33:12.0213590Z     Example:
2025-03-14T04:33:12.0213878Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-14T04:33:12.0214259Z         >>> # xdoctest: +REQUIRES(POSIX)
2025-03-14T04:33:12.0214615Z         >>> torch.hub.download_url_to_file(
2025-03-14T04:33:12.0215087Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2025-03-14T04:33:12.0215549Z         ...     "/tmp/temporary_file",
2025-03-14T04:33:12.0215868Z         ... )
2025-03-14T04:33:12.0216002Z 
2025-03-14T04:33:12.0216106Z     
2025-03-14T04:33:12.0216493Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0216862Z 
2025-03-14T04:33:12.0217365Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=816.
2025-03-14T04:33:12.0218235Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0218805Z Loads the Torch serialized object at the given URL.
2025-03-14T04:33:12.0219067Z 
2025-03-14T04:33:12.0219268Z     If downloaded file is a zip file, it will be automatically
2025-03-14T04:33:12.0219673Z     decompressed.
2025-03-14T04:33:12.0219821Z 
2025-03-14T04:33:12.0220053Z     If the object is already present in `model_dir`, it's deserialized and
2025-03-14T04:33:12.0220490Z     returned.
2025-03-14T04:33:12.0220854Z     The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2025-03-14T04:33:12.0221396Z     ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2025-03-14T04:33:12.0221709Z 
2025-03-14T04:33:12.0221817Z     Args:
2025-03-14T04:33:12.0222081Z         url (str): URL of the object to download
2025-03-14T04:33:12.0222527Z         model_dir (str, optional): directory in which to save the object
2025-03-14T04:33:12.0223186Z         map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2025-03-14T04:33:12.0223918Z         progress (bool, optional): whether or not to display a progress bar to stderr.
2025-03-14T04:33:12.0224402Z             Default: True
2025-03-14T04:33:12.0224909Z         check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2025-03-14T04:33:12.0225584Z             ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2025-03-14T04:33:12.0226158Z             digits of the SHA256 hash of the contents of the file. The hash is used to
2025-03-14T04:33:12.0226767Z             ensure unique names and to verify the contents of the file.
2025-03-14T04:33:12.0227184Z             Default: False
2025-03-14T04:33:12.0227705Z         file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2025-03-14T04:33:12.0228485Z         weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2025-03-14T04:33:12.0229194Z             Recommended for untrusted sources. See :func:`~torch.load` for more details.
2025-03-14T04:33:12.0229586Z 
2025-03-14T04:33:12.0229680Z     Example:
2025-03-14T04:33:12.0229966Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-14T04:33:12.0230381Z         >>> state_dict = torch.hub.load_state_dict_from_url(
2025-03-14T04:33:12.0230878Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2025-03-14T04:33:12.0231310Z         ... )
2025-03-14T04:33:12.0231444Z 
2025-03-14T04:33:12.0231550Z     
2025-03-14T04:33:12.0231941Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0232307Z 
2025-03-14T04:33:12.0260154Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=376.
2025-03-14T04:33:12.0261024Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:12.0261642Z Registers the function implementation as the fallback for the given key.
2025-03-14T04:33:12.0262000Z 
2025-03-14T04:33:12.0262237Z         This function only works for a library with global namespace ("_").
2025-03-14T04:33:12.0262573Z 
2025-03-14T04:33:12.0262681Z         Args:
2025-03-14T04:33:12.0263111Z             fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2025-03-14T04:33:12.0263636Z                 to register a fallthrough.
2025-03-14T04:33:12.0264198Z             dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2025-03-14T04:33:12.0264838Z                           the dispatch key that the library was created with.
2025-03-14T04:33:12.0265507Z             with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2025-03-14T04:33:12.0339255Z                          to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2025-03-14T04:33:12.0339724Z 
2025-03-14T04:33:12.0339955Z         Example::
2025-03-14T04:33:12.0340246Z             >>> my_lib = Library("_", "IMPL")
2025-03-14T04:33:12.0340629Z             >>> def fallback_kernel(op, *args, **kwargs):
2025-03-14T04:33:12.0341039Z             >>>     # Handle all autocast ops generically
2025-03-14T04:33:12.0341396Z             >>>     # ...
2025-03-14T04:33:12.0341728Z             >>> my_lib.fallback(fallback_kernel, "Autocast")
2025-03-14T04:33:12.0342094Z         
2025-03-14T04:33:12.0342871Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2025-03-14T04:33:12.0343604Z 
2025-03-14T04:33:12.0343761Z my_lib.fallback(fallback_kernel, "Autocast")
2025-03-14T04:33:12.0344110Z ^
2025-03-14T04:33:12.0344720Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=920.
2025-03-14T04:33:12.0345566Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:12.0346175Z Register a FakeTensor implementation ("fake impl") for this operator.
2025-03-14T04:33:12.0346516Z 
2025-03-14T04:33:12.0346716Z     Also sometimes known as a "meta kernel", "abstract impl".
2025-03-14T04:33:12.0347007Z 
2025-03-14T04:33:12.0347271Z     An "FakeTensor implementation" specifies the behavior of this operator on
2025-03-14T04:33:12.0347876Z     Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2025-03-14T04:33:12.0348544Z     certain properties (sizes/strides/storage_offset/device), it specifies
2025-03-14T04:33:12.0349227Z     what the properties of the output Tensors are.
2025-03-14T04:33:12.0349506Z 
2025-03-14T04:33:12.0349744Z     The FakeTensor implementation has the same signature as the operator.
2025-03-14T04:33:12.0350590Z     It is run for both FakeTensors and meta tensors. To write a FakeTensor
2025-03-14T04:33:12.0351185Z     implementation, assume that all Tensor inputs to the operator are
2025-03-14T04:33:12.0351744Z     regular CPU/CUDA/Meta tensors, but they do not have storage, and
2025-03-14T04:33:12.0352280Z     you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2025-03-14T04:33:12.0352846Z     The FakeTensor implementation must consist of only PyTorch operations
2025-03-14T04:33:12.0353396Z     (and may not directly access the storage or data of any input or
2025-03-14T04:33:12.0353821Z     intermediate Tensors).
2025-03-14T04:33:12.0354018Z 
2025-03-14T04:33:12.0354178Z     This API may be used as a decorator (see examples).
2025-03-14T04:33:12.0354459Z 
2025-03-14T04:33:12.0354604Z     For a detailed guide on custom ops, please see
2025-03-14T04:33:12.0355174Z     https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2025-03-14T04:33:12.0355533Z 
2025-03-14T04:33:12.0355630Z     Examples:
2025-03-14T04:33:12.0355888Z         >>> import torch
2025-03-14T04:33:12.0356181Z         >>> import numpy as np
2025-03-14T04:33:12.0356494Z         >>> from torch import Tensor
2025-03-14T04:33:12.0356803Z         >>>
2025-03-14T04:33:12.0357178Z         >>> # Example 1: an operator without data-dependent output shape
2025-03-14T04:33:12.0357725Z         >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2025-03-14T04:33:12.0358295Z         >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-03-14T04:33:12.0358832Z         >>>     raise NotImplementedError("Implementation goes here")
2025-03-14T04:33:12.0359306Z         >>>
2025-03-14T04:33:12.0361388Z         >>> @torch.library.register_fake("mylib::custom_linear")
2025-03-14T04:33:12.0361804Z         >>> def _(x, weight, bias):
2025-03-14T04:33:12.0362137Z         >>>     assert x.dim() == 2
2025-03-14T04:33:12.0362479Z         >>>     assert weight.dim() == 2
2025-03-14T04:33:12.0362826Z         >>>     assert bias.dim() == 1
2025-03-14T04:33:12.0363180Z         >>>     assert x.shape[1] == weight.shape[1]
2025-03-14T04:33:12.0363642Z         >>>     assert weight.shape[0] == bias.shape[0]
2025-03-14T04:33:12.0364031Z         >>>     assert x.device == weight.device
2025-03-14T04:33:12.0364370Z         >>>
2025-03-14T04:33:12.0364633Z         >>>     return (x @ weight.t()) + bias
2025-03-14T04:33:12.0364964Z         >>>
2025-03-14T04:33:12.0365264Z         >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2025-03-14T04:33:12.0365679Z         >>>     x = torch.randn(2, 3)
2025-03-14T04:33:12.0366009Z         >>>     w = torch.randn(3, 3)
2025-03-14T04:33:12.0366331Z         >>>     b = torch.randn(3)
2025-03-14T04:33:12.0366685Z         >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2025-03-14T04:33:12.0367040Z         >>>
2025-03-14T04:33:12.0367290Z         >>> assert y.shape == (2, 3)
2025-03-14T04:33:12.0367597Z         >>>
2025-03-14T04:33:12.0367919Z         >>> # Example 2: an operator with data-dependent output shape
2025-03-14T04:33:12.0368447Z         >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2025-03-14T04:33:12.0368928Z         >>> def custom_nonzero(x: Tensor) -> Tensor:
2025-03-14T04:33:12.0369299Z         >>>     x_np = x.numpy(force=True)
2025-03-14T04:33:12.0369665Z         >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-03-14T04:33:12.0370061Z         >>>     return torch.tensor(res, device=x.device)
2025-03-14T04:33:12.0370413Z         >>>
2025-03-14T04:33:12.0370730Z         >>> @torch.library.register_fake("mylib::custom_nonzero")
2025-03-14T04:33:12.0371121Z         >>> def _(x):
2025-03-14T04:33:12.0371498Z         >>> # Number of nonzero-elements is data-dependent.
2025-03-14T04:33:12.0371974Z         >>> # Since we cannot peek at the data in an fake impl,
2025-03-14T04:33:12.0372419Z         >>> # we use the ctx object to construct a new symint that
2025-03-14T04:33:12.0372825Z         >>> # represents the data-dependent size.
2025-03-14T04:33:12.0373201Z         >>>     ctx = torch.library.get_ctx()
2025-03-14T04:33:12.0373565Z         >>>     nnz = ctx.new_dynamic_size()
2025-03-14T04:33:12.0373917Z         >>>     shape = [nnz, x.dim()]
2025-03-14T04:33:12.0374298Z         >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-03-14T04:33:12.0374679Z         >>>     return result
2025-03-14T04:33:12.0374968Z         >>>
2025-03-14T04:33:12.0375291Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-14T04:33:12.0375689Z         >>>
2025-03-14T04:33:12.0375947Z         >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2025-03-14T04:33:12.0395430Z         >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2025-03-14T04:33:12.0396227Z         >>> trace.print_readable()
2025-03-14T04:33:12.0396705Z         >>>
2025-03-14T04:33:12.0397185Z         >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2025-03-14T04:33:12.0397535Z 
2025-03-14T04:33:12.0397643Z     
2025-03-14T04:33:12.0398298Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2025-03-14T04:33:12.0398935Z 
2025-03-14T04:33:12.0399043Z _._ = None
2025-03-14T04:33:12.0399334Z ^
2025-03-14T04:33:12.0399970Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1041.
2025-03-14T04:33:12.0400855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0401395Z Register a backward formula for this custom op.
2025-03-14T04:33:12.0401653Z 
2025-03-14T04:33:12.0401879Z     In order for an operator to work with autograd, you need to register
2025-03-14T04:33:12.0402318Z     a backward formula:
2025-03-14T04:33:12.0402704Z     1. You must tell us how to compute gradients during the backward pass
2025-03-14T04:33:12.0403150Z     by providing us a "backward" function.
2025-03-14T04:33:12.0403605Z     2. If you need any values from the forward to compute gradients, you can
2025-03-14T04:33:12.0404091Z     use `setup_context` to save values for backward.
2025-03-14T04:33:12.0404360Z 
2025-03-14T04:33:12.0404637Z     ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2025-03-14T04:33:12.0405192Z     - ``grads`` is one or more gradients. The number of gradients matches
2025-03-14T04:33:12.0405645Z     the number of outputs of the operator.
2025-03-14T04:33:12.0406104Z     The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2025-03-14T04:33:12.0406685Z     :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2025-03-14T04:33:12.0407217Z     same as :meth:`torch.autograd.Function.backward`.
2025-03-14T04:33:12.0407497Z 
2025-03-14T04:33:12.0407711Z     ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2025-03-14T04:33:12.0408280Z     Please save quantities needed for backward onto the ``ctx`` object via
2025-03-14T04:33:12.0408865Z     either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2025-03-14T04:33:12.0409428Z     or assigning them as attributes of ``ctx``. If your custom op has
2025-03-14T04:33:12.0409968Z     kwarg-only arguments, we expect the signature of ``setup_context``
2025-03-14T04:33:12.0410500Z     to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2025-03-14T04:33:12.0410819Z 
2025-03-14T04:33:12.0411038Z     Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2025-03-14T04:33:12.0411612Z     they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2025-03-14T04:33:12.0412235Z     not depend on or mutate global state. If you need a non-traceable backward,
2025-03-14T04:33:12.0412851Z     you can make it a separate custom_op that you call inside ``backward_fn``.
2025-03-14T04:33:12.0413190Z 
2025-03-14T04:33:12.0413426Z     If you need different autograd behavior on different devices, then we
2025-03-14T04:33:12.0414002Z     recommend creating two different custom operators, one for each device
2025-03-14T04:33:12.0414585Z     that needs different behavior, and switching between them at runtime.
2025-03-14T04:33:12.0414922Z 
2025-03-14T04:33:12.0415035Z     Examples:
2025-03-14T04:33:12.0415279Z         >>> import torch
2025-03-14T04:33:12.0415562Z         >>> import numpy as np
2025-03-14T04:33:12.0415878Z         >>> from torch import Tensor
2025-03-14T04:33:12.0416188Z         >>>
2025-03-14T04:33:12.0416543Z         >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2025-03-14T04:33:12.0416990Z         >>> def numpy_sin(x: Tensor) -> Tensor:
2025-03-14T04:33:12.0417330Z         >>>     x_np = x.cpu().numpy()
2025-03-14T04:33:12.0417661Z         >>>     y_np = np.sin(x_np)
2025-03-14T04:33:12.0418035Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-14T04:33:12.0418433Z         >>>
2025-03-14T04:33:12.0418718Z         >>> def setup_context(ctx, inputs, output) -> Tensor:
2025-03-14T04:33:12.0419082Z         >>>     x, = inputs
2025-03-14T04:33:12.0419384Z         >>>     ctx.save_for_backward(x)
2025-03-14T04:33:12.0419703Z         >>>
2025-03-14T04:33:12.0420004Z         >>> def backward(ctx, grad):
2025-03-14T04:33:12.0420336Z         >>>     x, = ctx.saved_tensors
2025-03-14T04:33:12.0420663Z         >>>     return grad * x.cos()
2025-03-14T04:33:12.0420969Z         >>>
2025-03-14T04:33:12.0421232Z         >>> torch.library.register_autograd(
2025-03-14T04:33:12.0421659Z         ...     "mylib::numpy_sin", backward, setup_context=setup_context
2025-03-14T04:33:12.0422057Z         ... )
2025-03-14T04:33:12.0422292Z         >>>
2025-03-14T04:33:12.0422562Z         >>> x = torch.randn(3, requires_grad=True)
2025-03-14T04:33:12.0422910Z         >>> y = numpy_sin(x)
2025-03-14T04:33:12.0423277Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-14T04:33:12.0423705Z         >>> assert torch.allclose(grad_x, x.cos())
2025-03-14T04:33:12.0424042Z         >>>
2025-03-14T04:33:12.0424300Z         >>> # Example with a keyword-only arg
2025-03-14T04:33:12.0424743Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-14T04:33:12.0425261Z         >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2025-03-14T04:33:12.0425649Z         >>>     x_np = x.cpu().numpy()
2025-03-14T04:33:12.0425968Z         >>>     y_np = x_np * val
2025-03-14T04:33:12.0426331Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-14T04:33:12.0426703Z         >>>
2025-03-14T04:33:12.0427067Z         >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2025-03-14T04:33:12.0427555Z         >>>     ctx.val = keyword_only_inputs["val"]
2025-03-14T04:33:12.0427898Z         >>>
2025-03-14T04:33:12.0428147Z         >>> def backward(ctx, grad):
2025-03-14T04:33:12.0428475Z         >>>     return grad * ctx.val
2025-03-14T04:33:12.0428782Z         >>>
2025-03-14T04:33:12.0429046Z         >>> torch.library.register_autograd(
2025-03-14T04:33:12.0429473Z         ...     "mylib::numpy_mul", backward, setup_context=setup_context
2025-03-14T04:33:12.0429854Z         ... )
2025-03-14T04:33:12.0430088Z         >>>
2025-03-14T04:33:12.0430359Z         >>> x = torch.randn(3, requires_grad=True)
2025-03-14T04:33:12.0430714Z         >>> y = numpy_mul(x, val=3.14)
2025-03-14T04:33:12.0431112Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-14T04:33:12.0431586Z         >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2025-03-14T04:33:12.0431889Z 
2025-03-14T04:33:12.0431979Z     
2025-03-14T04:33:12.0432363Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0432742Z 
2025-03-14T04:33:12.0433302Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1455.
2025-03-14T04:33:12.0434148Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0434747Z Given an operator and some sample arguments, tests if the operator is
2025-03-14T04:33:12.0435185Z     registered correctly.
2025-03-14T04:33:12.0435377Z 
2025-03-14T04:33:12.0435587Z     That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2025-03-14T04:33:12.0436163Z     custom op, you specified metadata (e.g. mutability info) about the custom op
2025-03-14T04:33:12.0436755Z     and these APIs require that the functions you pass them satisfy certain
2025-03-14T04:33:12.0437338Z     properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2025-03-14T04:33:12.0437845Z     ``opcheck`` tests these metadata and properties.
2025-03-14T04:33:12.0438114Z 
2025-03-14T04:33:12.0438241Z     Concretely, we test the following:
2025-03-14T04:33:12.0438476Z 
2025-03-14T04:33:12.0438657Z     - test_schema: If the schema matches the implementation of
2025-03-14T04:33:12.0439289Z       the operator. For example: if the schema specifies a Tensor is mutated,
2025-03-14T04:33:12.0439852Z       then we check the implementation mutates the Tensor. If the schema
2025-03-14T04:33:12.0440382Z       specifies that we return a new Tensor, then we check that the
2025-03-14T04:33:12.0440922Z       implementation returns a new Tensor (instead of an existing one or
2025-03-14T04:33:12.0441381Z       a view of an existing one).
2025-03-14T04:33:12.0441795Z     - test_autograd_registration: If the operator supports training
2025-03-14T04:33:12.0442317Z       (autograd): we check that its autograd formula is registered via
2025-03-14T04:33:12.0442848Z       torch.library.register_autograd or a manual registration to one
2025-03-14T04:33:12.0443386Z       or more DispatchKey::Autograd keys. Any other DispatchKey-based
2025-03-14T04:33:12.0443853Z       registrations may lead to undefined behavior.
2025-03-14T04:33:12.0444294Z     - test_faketensor: If the operator has a FakeTensor kernel
2025-03-14T04:33:12.0444775Z       (and if it is correct). The FakeTensor kernel is necessary (
2025-03-14T04:33:12.0445292Z       but not sufficient) for the operator to work with PyTorch compilation
2025-03-14T04:33:12.0445850Z       APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2025-03-14T04:33:12.0446419Z       (also sometimes known as a meta kernel) was registered for the
2025-03-14T04:33:12.0446934Z       operator and that it is correct. This test takes the result of
2025-03-14T04:33:12.0447450Z       running the operator on real tensors and the result of running
2025-03-14T04:33:12.0447950Z       the operator on FakeTensors and checks that they have the same
2025-03-14T04:33:12.0448421Z       Tensor metadata (sizes/strides/dtype/device/etc).
2025-03-14T04:33:12.0449106Z     - test_aot_dispatch_dynamic: If the operator has correct behavior
2025-03-14T04:33:12.0449627Z       with PyTorch compilation APIs (torch.compile/export/FX).
2025-03-14T04:33:12.0450144Z       This checks that the outputs (and gradients, if applicable) are the
2025-03-14T04:33:12.0450636Z       same under eager-mode PyTorch and torch.compile.
2025-03-14T04:33:12.0451104Z       This test is a superset of ``test_faketensor`` and is an e2e test;
2025-03-14T04:33:12.0451591Z       other things it tests are that the operator supports
2025-03-14T04:33:12.0452095Z       functionalization and that the backward pass (if it exists) also
2025-03-14T04:33:12.0452577Z       supports FakeTensor and functionalization.
2025-03-14T04:33:12.0452839Z 
2025-03-14T04:33:12.0453040Z     For best results, please call ``opcheck`` multiple times with a
2025-03-14T04:33:12.0453543Z     representative set of inputs. If your operator supports
2025-03-14T04:33:12.0454084Z     autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2025-03-14T04:33:12.0454751Z     if your operator supports multiple devices (e.g. CPU and CUDA), please
2025-03-14T04:33:12.0455305Z     use ``opcheck`` with inputs on all supported devices.
2025-03-14T04:33:12.0455578Z 
2025-03-14T04:33:12.0455685Z     Args:
2025-03-14T04:33:12.0456012Z         op: The operator. Must either be a function decorated with
2025-03-14T04:33:12.0456540Z             :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2025-03-14T04:33:12.0457114Z             found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2025-03-14T04:33:12.0457574Z         args: The args to the operator
2025-03-14T04:33:12.0457917Z         kwargs: The kwargs to the operator
2025-03-14T04:33:12.0458335Z         test_utils: Tests that we should run. Default: all of them.
2025-03-14T04:33:12.0458787Z             Example: ("test_schema", "test_faketensor")
2025-03-14T04:33:12.0459249Z         raise_exception: If we should raise an exception on the first
2025-03-14T04:33:12.0459739Z             error. If False, we will return a dict with information
2025-03-14T04:33:12.0460150Z             on if each test passed or not.
2025-03-14T04:33:12.0460611Z         rtol (Optional[float]): Relative tolerance for floating point comparisons.
2025-03-14T04:33:12.0461167Z             If specified ``atol`` must also be specified.
2025-03-14T04:33:12.0461621Z             If omitted, default values based on the ``dtype`` are selected
2025-03-14T04:33:12.0462116Z             (see the table in :func:`torch.testing.assert_close`).
2025-03-14T04:33:12.0462649Z         atol (Optional[float]): Absolute tolerance for floating point comparisons.
2025-03-14T04:33:12.0463162Z             If specified ``rtol`` must also be specified.
2025-03-14T04:33:12.0463617Z             If omitted, default values based on the ``dtype`` are selected
2025-03-14T04:33:12.0464108Z             (see the table in :func:`torch.testing.assert_close`).
2025-03-14T04:33:12.0464401Z 
2025-03-14T04:33:12.0464503Z     .. warning::
2025-03-14T04:33:12.0464658Z 
2025-03-14T04:33:12.0464882Z         opcheck and :func:`torch.autograd.gradcheck` test different things;
2025-03-14T04:33:12.0465434Z         opcheck tests if your usage of torch.library APIs is correct while
2025-03-14T04:33:12.0465983Z         :func:`torch.autograd.gradcheck` tests if your autograd formula is
2025-03-14T04:33:12.0466540Z         mathematically correct. Use both to test custom ops that support
2025-03-14T04:33:12.0466993Z         gradient computation.
2025-03-14T04:33:12.0467197Z 
2025-03-14T04:33:12.0467330Z     Example:
2025-03-14T04:33:12.0467475Z 
2025-03-14T04:33:12.0467618Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:12.0468091Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-14T04:33:12.0468563Z         >>> def numpy_mul(x: Tensor, y: float) -> Tensor:
2025-03-14T04:33:12.0468945Z         >>>     x_np = x.numpy(force=True)
2025-03-14T04:33:12.0469286Z         >>>     z_np = x_np * y
2025-03-14T04:33:12.0469627Z         >>>     return torch.from_numpy(z_np).to(x.device)
2025-03-14T04:33:12.0469982Z         >>>
2025-03-14T04:33:12.0470238Z         >>> @numpy_mul.register_fake
2025-03-14T04:33:12.0470552Z         >>> def _(x, y):
2025-03-14T04:33:12.0470852Z         >>>     return torch.empty_like(x)
2025-03-14T04:33:12.0471173Z         >>>
2025-03-14T04:33:12.0471443Z         >>> def setup_context(ctx, inputs, output):
2025-03-14T04:33:12.0471794Z         >>>     y, = inputs
2025-03-14T04:33:12.0472074Z         >>>     ctx.y = y
2025-03-14T04:33:12.0472346Z         >>>
2025-03-14T04:33:12.0472588Z         >>> def backward(ctx, grad):
2025-03-14T04:33:12.0472909Z         >>>     return grad * ctx.y, None
2025-03-14T04:33:12.0473210Z         >>>
2025-03-14T04:33:12.0473570Z         >>> numpy_mul.register_autograd(backward, setup_context=setup_context)
2025-03-14T04:33:12.0474003Z         >>>
2025-03-14T04:33:12.0474250Z         >>> sample_inputs = [
2025-03-14T04:33:12.0474558Z         >>>     (torch.randn(3), 3.14),
2025-03-14T04:33:12.0474973Z         >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2025-03-14T04:33:12.0475410Z         >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2025-03-14T04:33:12.0475878Z         >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2025-03-14T04:33:12.0476279Z         >>> ]
2025-03-14T04:33:12.0476511Z         >>>
2025-03-14T04:33:12.0476750Z         >>> for args in sample_inputs:
2025-03-14T04:33:12.0477113Z         >>>     torch.library.opcheck(numpy_mul, args)
2025-03-14T04:33:12.0477377Z 
2025-03-14T04:33:12.0477467Z     
2025-03-14T04:33:12.0477845Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0478224Z 
2025-03-14T04:33:12.0827919Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1283.
2025-03-14T04:33:12.0829083Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.0829830Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args)
2025-03-14T04:33:12.0830295Z 
2025-03-14T04:33:12.0830483Z     Loads an object saved with :func:`torch.save` from a file.
2025-03-14T04:33:12.0830978Z 
2025-03-14T04:33:12.0831220Z     :func:`torch.load` uses Python's unpickling facilities but treats storages,
2025-03-14T04:33:12.0831816Z     which underlie tensors, specially. They are first deserialized on the
2025-03-14T04:33:12.0832403Z     CPU and are then moved to the device they were saved from. If this fails
2025-03-14T04:33:12.0832986Z     (e.g. because the run time system doesn't have certain devices), an exception
2025-03-14T04:33:12.0833589Z     is raised. However, storages can be dynamically remapped to an alternative
2025-03-14T04:33:12.0834125Z     set of devices using the :attr:`map_location` argument.
2025-03-14T04:33:12.0834420Z 
2025-03-14T04:33:12.0834667Z     If :attr:`map_location` is a callable, it will be called once for each serialized
2025-03-14T04:33:12.0835272Z     storage with two arguments: storage and location. The storage argument
2025-03-14T04:33:12.0835854Z     will be the initial deserialization of the storage, residing on the CPU.
2025-03-14T04:33:12.0836434Z     Each serialized storage has a location tag associated with it which
2025-03-14T04:33:12.0836993Z     identifies the device it was saved from, and this tag is the second
2025-03-14T04:33:12.0837590Z     argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2025-03-14T04:33:12.0838260Z     for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2025-03-14T04:33:12.0838825Z     :attr:`map_location` should return either ``None`` or a storage. If
2025-03-14T04:33:12.0839470Z     :attr:`map_location` returns a storage, it will be used as the final deserialized
2025-03-14T04:33:12.0840108Z     object, already moved to the right device. Otherwise, :func:`torch.load` will
2025-03-14T04:33:12.0840738Z     fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2025-03-14T04:33:12.0841108Z 
2025-03-14T04:33:12.0841365Z     If :attr:`map_location` is a :class:`torch.device` object or a string containing
2025-03-14T04:33:12.0841978Z     a device tag, it indicates the location where all tensors should be loaded.
2025-03-14T04:33:12.0842379Z 
2025-03-14T04:33:12.0842640Z     Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2025-03-14T04:33:12.0843244Z     appearing in the file (keys), to ones that specify where to put the
2025-03-14T04:33:12.0843682Z     storages (values).
2025-03-14T04:33:12.0843848Z 
2025-03-14T04:33:12.0844089Z     User extensions can register their own location tags and tagging and
2025-03-14T04:33:12.0844707Z     deserialization methods using :func:`torch.serialization.register_package`.
2025-03-14T04:33:12.0845085Z 
2025-03-14T04:33:12.0845190Z     Args:
2025-03-14T04:33:12.0845645Z         f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2025-03-14T04:33:12.0846392Z             or a string or os.PathLike object containing a file name
2025-03-14T04:33:12.0847019Z         map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2025-03-14T04:33:12.0847569Z             locations
2025-03-14T04:33:12.0847969Z         pickle_module: module used for unpickling metadata and objects (has to
2025-03-14T04:33:12.0848501Z             match the :attr:`pickle_module` used to serialize file)
2025-03-14T04:33:12.0849176Z         weights_only: Indicates whether unpickler should be restricted to
2025-03-14T04:33:12.0849687Z             loading only tensors, primitive types, dictionaries
2025-03-14T04:33:12.0850201Z             and any types added via :func:`torch.serialization.add_safe_globals`.
2025-03-14T04:33:12.0850690Z             See :ref:`weights-only` for more details.
2025-03-14T04:33:12.0851283Z         mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory.
2025-03-14T04:33:12.0852082Z             Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2025-03-14T04:33:12.0852920Z             are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2025-03-14T04:33:12.0853711Z             second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2025-03-14T04:33:12.0854392Z             tensor storages from disk to CPU memory in the first step, ``f`` is mmaped.
2025-03-14T04:33:12.0855011Z         pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2025-03-14T04:33:12.0855609Z             :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2025-03-14T04:33:12.0856070Z             :attr:`errors=...`.
2025-03-14T04:33:12.0856276Z 
2025-03-14T04:33:12.0856396Z     .. warning::
2025-03-14T04:33:12.0856773Z         :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2025-03-14T04:33:12.0857330Z         uses ``pickle`` module implicitly, which is known to be insecure.
2025-03-14T04:33:12.0857936Z         It is possible to construct malicious pickle data which will execute arbitrary code
2025-03-14T04:33:12.0858587Z         during unpickling. Never load data that could have come from an untrusted
2025-03-14T04:33:12.0859249Z         source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2025-03-14T04:33:12.0859671Z 
2025-03-14T04:33:12.0859808Z     .. note::
2025-03-14T04:33:12.0860218Z         When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2025-03-14T04:33:12.0860871Z         will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2025-03-14T04:33:12.0861532Z         and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2025-03-14T04:33:12.0861937Z 
2025-03-14T04:33:12.0862031Z     .. note::
2025-03-14T04:33:12.0862435Z         By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2025-03-14T04:33:12.0863041Z         case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2025-03-14T04:33:12.0863603Z         when loading files saved by Python 2 in Python 3.  If this default
2025-03-14T04:33:12.0864203Z         is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2025-03-14T04:33:12.0864842Z         these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2025-03-14T04:33:12.0865463Z         to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2025-03-14T04:33:12.0866067Z         as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2025-03-14T04:33:12.0866417Z 
2025-03-14T04:33:12.0866531Z     Example:
2025-03-14T04:33:12.0866827Z         >>> # xdoctest: +SKIP("undefined filepaths")
2025-03-14T04:33:12.0867241Z         >>> torch.load("tensors.pt", weights_only=True)
2025-03-14T04:33:12.0867688Z         # Load all tensors onto the CPU
2025-03-14T04:33:12.0868063Z         >>> torch.load(
2025-03-14T04:33:12.0868350Z         ...     "tensors.pt",
2025-03-14T04:33:12.0868689Z         ...     map_location=torch.device("cpu"),
2025-03-14T04:33:12.0869057Z         ...     weights_only=True,
2025-03-14T04:33:12.0869351Z         ... )
2025-03-14T04:33:12.0869652Z         # Load all tensors onto the CPU, using a function
2025-03-14T04:33:12.0870022Z         >>> torch.load(
2025-03-14T04:33:12.0870311Z         ...     "tensors.pt",
2025-03-14T04:33:12.0870658Z         ...     map_location=lambda storage, loc: storage,
2025-03-14T04:33:12.0871027Z         ...     weights_only=True,
2025-03-14T04:33:12.0871329Z         ... )
2025-03-14T04:33:12.0871588Z         # Load all tensors onto GPU 1
2025-03-14T04:33:12.0871906Z         >>> torch.load(
2025-03-14T04:33:12.0872183Z         ...     "tensors.pt",
2025-03-14T04:33:12.0872543Z         ...     map_location=lambda storage, loc: storage.cuda(1),
2025-03-14T04:33:12.0872944Z         ...     weights_only=True,
2025-03-14T04:33:12.0873272Z         ... )  # type: ignore[attr-defined]
2025-03-14T04:33:12.0873624Z         # Map tensors from GPU 1 to GPU 0
2025-03-14T04:33:12.0873987Z         >>> torch.load(
2025-03-14T04:33:12.0874263Z         ...     "tensors.pt",
2025-03-14T04:33:12.0874572Z         ...     map_location={"cuda:1": "cuda:0"},
2025-03-14T04:33:12.0874926Z         ...     weights_only=True,
2025-03-14T04:33:12.0875227Z         ... )
2025-03-14T04:33:12.0875499Z         # Load tensor from io.BytesIO object
2025-03-14T04:33:12.0875996Z         # Loading from a buffer setting weights_only=False, warning this can be unsafe
2025-03-14T04:33:12.0876503Z         >>> with open("tensor.pt", "rb") as f:
2025-03-14T04:33:12.0876865Z         ...     buffer = io.BytesIO(f.read())
2025-03-14T04:33:12.0877231Z         >>> torch.load(buffer, weights_only=False)
2025-03-14T04:33:12.0877640Z         # Load a module with 'ascii' encoding for unpickling
2025-03-14T04:33:12.0878172Z         # Loading from a module setting weights_only=False, warning this can be unsafe
2025-03-14T04:33:12.0878783Z         >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2025-03-14T04:33:12.0879582Z     
2025-03-14T04:33:12.0880072Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.0880444Z 
2025-03-14T04:33:12.1944389Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=38.
2025-03-14T04:33:12.1946131Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:12.1947273Z Check if the current accelerator is available at runtime: it was build, all the
2025-03-14T04:33:12.1948367Z     required drivers are available and at least one device is visible.
2025-03-14T04:33:12.1949466Z     See :ref:`accelerator<accelerators>` for details.
2025-03-14T04:33:12.1949976Z 
2025-03-14T04:33:12.1950139Z     Returns:
2025-03-14T04:33:12.1950947Z         bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2025-03-14T04:33:12.1951723Z 
2025-03-14T04:33:12.1951918Z     Example::
2025-03-14T04:33:12.1952186Z 
2025-03-14T04:33:12.1952707Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:12.1953615Z     
2025-03-14T04:33:12.1954939Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2025-03-14T04:33:12.1956261Z 
2025-03-14T04:33:12.1956718Z assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:12.1957615Z                                         ^
2025-03-14T04:33:12.1966771Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=153.
2025-03-14T04:33:12.1968488Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:12.1969848Z Wait for all kernels in all streams on the given device to complete.
2025-03-14T04:33:12.1970490Z 
2025-03-14T04:33:12.1970654Z     Args:
2025-03-14T04:33:12.1971465Z         device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2025-03-14T04:33:12.1972661Z             the current :ref:`accelerator<accelerators>` device type. If not given,
2025-03-14T04:33:12.1973668Z             use :func:`torch.accelerator.current_device_index` by default.
2025-03-14T04:33:12.1974236Z 
2025-03-14T04:33:12.1974825Z     .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2025-03-14T04:33:12.1975612Z 
2025-03-14T04:33:12.1975796Z     Example::
2025-03-14T04:33:12.1976043Z 
2025-03-14T04:33:12.1976317Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:12.1977267Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:12.1978263Z         >>> start_event = torch.Event(enable_timing=True)
2025-03-14T04:33:12.1978990Z         >>> end_event = torch.Event(enable_timing=True)
2025-03-14T04:33:12.1979813Z         >>> start_event.record()
2025-03-14T04:33:12.1980604Z         >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2025-03-14T04:33:12.1981494Z         >>> sum = torch.sum(tensor)
2025-03-14T04:33:12.1982066Z         >>> end_event.record()
2025-03-14T04:33:12.1982637Z         >>> torch.accelerator.synchronize()
2025-03-14T04:33:12.1983372Z         >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2025-03-14T04:33:12.1984105Z     
2025-03-14T04:33:12.1985390Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2025-03-14T04:33:12.1986686Z 
2025-03-14T04:33:12.1987195Z assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:12.1988127Z                                         ^
2025-03-14T04:33:12.2198874Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=396.
2025-03-14T04:33:12.2200653Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:12.2201607Z Retrieves the CUDA runtime API module.
2025-03-14T04:33:12.2202036Z 
2025-03-14T04:33:12.2202043Z 
2025-03-14T04:33:12.2202722Z     This function initializes the CUDA runtime environment if it is not already
2025-03-14T04:33:12.2203904Z     initialized and returns the CUDA runtime API module (_cudart). The CUDA
2025-03-14T04:33:12.2205011Z     runtime API module provides access to various CUDA runtime functions.
2025-03-14T04:33:12.2205677Z 
2025-03-14T04:33:12.2205860Z     Args:
2025-03-14T04:33:12.2206260Z         ``None``
2025-03-14T04:33:12.2206524Z 
2025-03-14T04:33:12.2206680Z     Returns:
2025-03-14T04:33:12.2207190Z         module: The CUDA runtime API module (_cudart).
2025-03-14T04:33:12.2207709Z 
2025-03-14T04:33:12.2207879Z     Raises:
2025-03-14T04:33:12.2208559Z         RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2025-03-14T04:33:12.2209936Z         AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2025-03-14T04:33:12.2210886Z 
2025-03-14T04:33:12.2211136Z     Example of CUDA operations with profiling:
2025-03-14T04:33:12.2211784Z         >>> import torch
2025-03-14T04:33:12.2212366Z         >>> from torch.cuda import cudart, check_error
2025-03-14T04:33:12.2213011Z         >>> import os
2025-03-14T04:33:12.2213453Z         >>>
2025-03-14T04:33:12.2213898Z         >>> os.environ['CUDA_PROFILE'] = '1'
2025-03-14T04:33:12.2214482Z         >>>
2025-03-14T04:33:12.2214966Z         >>> def perform_cuda_operations_with_streams():
2025-03-14T04:33:12.2215654Z         >>>     stream = torch.cuda.Stream()
2025-03-14T04:33:12.2216298Z         >>>     with torch.cuda.stream(stream):
2025-03-14T04:33:12.2217202Z         >>>         x = torch.randn(100, 100, device='cuda')
2025-03-14T04:33:12.2217901Z         >>>         y = torch.randn(100, 100, device='cuda')
2025-03-14T04:33:12.2218563Z         >>>         z = torch.mul(x, y)
2025-03-14T04:33:12.2219133Z         >>>     return z
2025-03-14T04:33:12.2219582Z         >>>
2025-03-14T04:33:12.2220025Z         >>> torch.cuda.synchronize()
2025-03-14T04:33:12.2220681Z         >>> print("====== Start nsys profiling ======")
2025-03-14T04:33:12.2221424Z         >>> check_error(cudart().cudaProfilerStart())
2025-03-14T04:33:12.2222170Z         >>> with torch.autograd.profiler.emit_nvtx():
2025-03-14T04:33:12.2222943Z         >>>     result = perform_cuda_operations_with_streams()
2025-03-14T04:33:12.2223700Z         >>>     print("CUDA operations completed.")
2025-03-14T04:33:12.2224467Z         >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2025-03-14T04:33:12.2225249Z         >>> print("====== End nsys profiling ======")
2025-03-14T04:33:12.2225728Z 
2025-03-14T04:33:12.2226113Z     To run this example and save the profiling information, execute:
2025-03-14T04:33:12.2227402Z         >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-14T04:33:12.2228449Z 
2025-03-14T04:33:12.2228926Z     This command profiles the CUDA operations in the provided script and saves
2025-03-14T04:33:12.2229999Z     the profiling information to a file named `trace_name.prof`.
2025-03-14T04:33:12.2231065Z     The `--profile-from-start off` option ensures that profiling starts only
2025-03-14T04:33:12.2232020Z     after the `cudaProfilerStart` call in the script.
2025-03-14T04:33:12.2232949Z     The `--csv` and `--print-summary` options format the profiling output as a
2025-03-14T04:33:12.2233863Z     CSV file and print a summary, respectively.
2025-03-14T04:33:12.2234810Z     The `-o` option specifies the output file name, and the `-f` option forces the
2025-03-14T04:33:12.2235816Z     overwrite of the output file if it already exists.
2025-03-14T04:33:12.2236511Z     
2025-03-14T04:33:12.2237984Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2025-03-14T04:33:12.2239563Z 
2025-03-14T04:33:12.2240262Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-14T04:33:12.2241332Z ^
2025-03-14T04:33:12.2340537Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=105.
2025-03-14T04:33:12.2342283Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.2343014Z 
2025-03-14T04:33:12.2343470Z Append the given callback function to this ``Future``, which will be run
2025-03-14T04:33:12.2344445Z when the ``Future`` is completed.  Multiple callbacks can be added to
2025-03-14T04:33:12.2345444Z the same ``Future``, but the order in which they will be executed cannot
2025-03-14T04:33:12.2346415Z be guaranteed (to enforce a certain order consider chaining:
2025-03-14T04:33:12.2347354Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2025-03-14T04:33:12.2348387Z is the reference to this ``Future``. The callback function can use the
2025-03-14T04:33:12.2349596Z :meth:`value` method to get the value. Note that if this ``Future`` is
2025-03-14T04:33:12.2350619Z already completed, the given callback will be run immediately inline.
2025-03-14T04:33:12.2351233Z 
2025-03-14T04:33:12.2351620Z If the ``Future``'s value contains tensors that reside on GPUs, the
2025-03-14T04:33:12.2352623Z callback might be invoked while the async kernels that are populating
2025-03-14T04:33:12.2353681Z those tensors haven't yet finished executing on the device. However, the
2025-03-14T04:33:12.2354744Z callback will be invoked with some dedicated streams set as current
2025-03-14T04:33:12.2355859Z (fetched from a global pool) which will be synchronized with those
2025-03-14T04:33:12.2356942Z kernels. Hence any operation performed by the callback on these tensors
2025-03-14T04:33:12.2357970Z will be scheduled on the device after the kernels complete. In other
2025-03-14T04:33:12.2358945Z words, as long as the callback doesn't switch streams, it can safely
2025-03-14T04:33:12.2360051Z manipulate the result without any additional synchronization. This is
2025-03-14T04:33:12.2360979Z similar to the non-blocking behavior of :meth:`wait`.
2025-03-14T04:33:12.2361505Z 
2025-03-14T04:33:12.2361889Z Similarly, if the callback returns a value that contains tensors that
2025-03-14T04:33:12.2362875Z reside on a GPU, it can do so even if the kernels that are producing
2025-03-14T04:33:12.2363895Z these tensors are still running on the device, as long as the callback
2025-03-14T04:33:12.2364918Z didn't change streams during its execution. If one wants to change
2025-03-14T04:33:12.2365904Z streams, one must be careful to re-synchronize them with the original
2025-03-14T04:33:12.2366964Z streams, that is, those that were current when the callback was invoked.
2025-03-14T04:33:12.2367620Z 
2025-03-14T04:33:12.2367917Z Args:
2025-03-14T04:33:12.2368532Z     callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2025-03-14T04:33:12.2369353Z                             the only argument.
2025-03-14T04:33:12.2369756Z 
2025-03-14T04:33:12.2369918Z Returns:
2025-03-14T04:33:12.2370451Z     A new ``Future`` object that holds the return value of the
2025-03-14T04:33:12.2371318Z     ``callback`` and will be marked as completed when the given
2025-03-14T04:33:12.2372060Z     ``callback`` finishes.
2025-03-14T04:33:12.2372368Z 
2025-03-14T04:33:12.2372730Z .. note:: Note that if the callback function throws, either
2025-03-14T04:33:12.2373676Z     through the original future being completed with an exception and
2025-03-14T04:33:12.2374651Z     calling ``fut.wait()``, or through other code in the callback, the
2025-03-14T04:33:12.2375636Z     future returned by ``then`` will be marked appropriately with the
2025-03-14T04:33:12.2376620Z     encountered error. However, if this callback later completes
2025-03-14T04:33:12.2377612Z     additional futures, those futures are not marked as completed with
2025-03-14T04:33:12.2378581Z     an error and the user is responsible for handling completion/waiting
2025-03-14T04:33:12.2379385Z     on those futures independently.
2025-03-14T04:33:12.2379758Z 
2025-03-14T04:33:12.2379960Z Example::
2025-03-14T04:33:12.2380572Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-14T04:33:12.2381246Z     >>> def callback(fut):
2025-03-14T04:33:12.2381843Z     ...     print(f"RPC return value is {fut.wait()}.")
2025-03-14T04:33:12.2382510Z     >>> fut = torch.futures.Future()
2025-03-14T04:33:12.2383250Z     >>> # The inserted callback will print the return value when
2025-03-14T04:33:12.2384038Z     >>> # receiving the response from "worker1"
2025-03-14T04:33:12.2384688Z     >>> cb_fut = fut.then(callback)
2025-03-14T04:33:12.2385288Z     >>> chain_cb_fut = cb_fut.then(
2025-03-14T04:33:12.2385943Z     ...     lambda x : print(f"Chained cb done. {x.wait()}")
2025-03-14T04:33:12.2386636Z     ... )
2025-03-14T04:33:12.2387048Z     >>> fut.set_result(5)
2025-03-14T04:33:12.2387574Z     RPC return value is 5.
2025-03-14T04:33:12.2388108Z     Chained cb done. None
2025-03-14T04:33:12.2388426Z 
2025-03-14T04:33:12.2388892Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.2389568Z 
2025-03-14T04:33:12.2390583Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=213.
2025-03-14T04:33:12.2392210Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.2392914Z 
2025-03-14T04:33:12.2393318Z Set the result for this ``Future``, which will mark this ``Future`` as
2025-03-14T04:33:12.2394337Z completed and trigger all attached callbacks. Note that a ``Future``
2025-03-14T04:33:12.2395362Z cannot be marked completed twice.
2025-03-14T04:33:12.2395750Z 
2025-03-14T04:33:12.2396183Z If the result contains tensors that reside on GPUs, this method can be
2025-03-14T04:33:12.2397194Z called even if the asynchronous kernels that are populating those
2025-03-14T04:33:12.2398231Z tensors haven't yet completed running on the device, provided that the
2025-03-14T04:33:12.2399404Z streams on which those kernels were enqueued are set as the current ones
2025-03-14T04:33:12.2400446Z when this method is called. Put simply, it's safe to call this method
2025-03-14T04:33:12.2401484Z immediately after launching those kernels, without any additional
2025-03-14T04:33:12.2402499Z synchronization, as long as one doesn't change streams in between. This
2025-03-14T04:33:12.2403481Z method will record events on all the relevant current streams and will
2025-03-14T04:33:12.2404496Z use them to ensure proper scheduling for all the consumers of this
2025-03-14T04:33:12.2405257Z ``Future``.
2025-03-14T04:33:12.2405500Z 
2025-03-14T04:33:12.2405653Z Args:
2025-03-14T04:33:12.2406167Z     result (object): the result object of this ``Future``.
2025-03-14T04:33:12.2406826Z 
2025-03-14T04:33:12.2407013Z Example::
2025-03-14T04:33:12.2407529Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-14T04:33:12.2408230Z     >>> import threading
2025-03-14T04:33:12.2408720Z     >>> import time
2025-03-14T04:33:12.2409210Z     >>> def slow_set_future(fut, value):
2025-03-14T04:33:12.2409829Z     ...     time.sleep(0.5)
2025-03-14T04:33:12.2410308Z     ...     fut.set_result(value)
2025-03-14T04:33:12.2410876Z     >>> fut = torch.futures.Future()
2025-03-14T04:33:12.2411462Z     >>> t = threading.Thread(
2025-03-14T04:33:12.2412015Z     ...     target=slow_set_future,
2025-03-14T04:33:12.2412609Z     ...     args=(fut, torch.ones(2) * 3)
2025-03-14T04:33:12.2413188Z     ... )
2025-03-14T04:33:12.2413593Z     >>> t.start()
2025-03-14T04:33:12.2414054Z     >>> print(fut.wait())
2025-03-14T04:33:12.2414553Z     tensor([3., 3.])
2025-03-14T04:33:12.2415003Z     >>> t.join()
2025-03-14T04:33:12.2415284Z 
2025-03-14T04:33:12.2415766Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.2416491Z 
2025-03-14T04:33:12.2477492Z msg = Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144.
2025-03-14T04:33:12.2479468Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.2480640Z Compiles compute shader from source and allows one to invoke kernels
2025-03-14T04:33:12.2481553Z     defined there from the comfort of Python runtime
2025-03-14T04:33:12.2482237Z     Example::
2025-03-14T04:33:12.2482506Z 
2025-03-14T04:33:12.2482756Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS)
2025-03-14T04:33:12.2483472Z         >>> lib = torch.mps.compile_shader(
2025-03-14T04:33:12.2484643Z         ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }"
2025-03-14T04:33:12.2485732Z         ...  )
2025-03-14T04:33:12.2486209Z         >>> x = torch.zeros(16, device="mps")
2025-03-14T04:33:12.2486838Z         >>> lib.full(x, 3.14)
2025-03-14T04:33:12.2487331Z     
2025-03-14T04:33:12.2488027Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.2488748Z 
2025-03-14T04:33:12.2655545Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202.
2025-03-14T04:33:12.2656437Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.2656984Z Return the sum of each row of the given sparse tensor.
2025-03-14T04:33:12.2657277Z 
2025-03-14T04:33:12.2657511Z     Returns the sum of each row of the sparse tensor :attr:`input` in the given
2025-03-14T04:33:12.2658078Z     dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2025-03-14T04:33:12.2658783Z     reduce over all of them. When sum over all ``sparse_dim``, this method
2025-03-14T04:33:12.2659288Z     returns a dense tensor instead of a sparse tensor.
2025-03-14T04:33:12.2659580Z 
2025-03-14T04:33:12.2659841Z     All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2025-03-14T04:33:12.2660474Z     tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2025-03-14T04:33:12.2660787Z 
2025-03-14T04:33:12.2661038Z     During backward, only gradients at ``nnz`` locations of :attr:`input`
2025-03-14T04:33:12.2661620Z     will propagate back. Note that the gradients of :attr:`input` is coalesced.
2025-03-14T04:33:12.2661993Z 
2025-03-14T04:33:12.2662090Z     Args:
2025-03-14T04:33:12.2662370Z         input (Tensor): the input sparse tensor
2025-03-14T04:33:12.2662903Z         dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2025-03-14T04:33:12.2663418Z             over all dims.
2025-03-14T04:33:12.2663866Z         dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2025-03-14T04:33:12.2664383Z             Default: dtype of :attr:`input`.
2025-03-14T04:33:12.2664687Z 
2025-03-14T04:33:12.2664799Z     Example::
2025-03-14T04:33:12.2664957Z 
2025-03-14T04:33:12.2665051Z         >>> nnz = 3
2025-03-14T04:33:12.2665313Z         >>> dims = [5, 5, 2, 3]
2025-03-14T04:33:12.2665677Z         >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2025-03-14T04:33:12.2666163Z                            torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2025-03-14T04:33:12.2666613Z         >>> V = torch.randn(nnz, dims[2], dims[3])
2025-03-14T04:33:12.2666972Z         >>> size = torch.Size(dims)
2025-03-14T04:33:12.2667433Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:12.2667838Z         >>> S = torch.sparse_coo_tensor(I, V, size)
2025-03-14T04:33:12.2668179Z         >>> S
2025-03-14T04:33:12.2668441Z         tensor(indices=tensor([[2, 0, 3],
2025-03-14T04:33:12.2668782Z                                [2, 4, 1]]),
2025-03-14T04:33:12.2669142Z                values=tensor([[[-0.6438, -1.6467,  1.4004],
2025-03-14T04:33:12.2669525Z                                [ 0.3411,  0.0918, -0.2312]],
2025-03-14T04:33:12.2669753Z 
2025-03-14T04:33:12.2669885Z                               [[ 0.5348,  0.0634, -2.0494],
2025-03-14T04:33:12.2670246Z                                [-0.7125, -1.0646,  2.1844]],
2025-03-14T04:33:12.2670474Z 
2025-03-14T04:33:12.2670661Z                               [[ 0.1276,  0.1874, -0.6334],
2025-03-14T04:33:12.2671009Z                                [-1.9682, -0.5340,  0.7483]]]),
2025-03-14T04:33:12.2671407Z                size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2025-03-14T04:33:12.2671683Z 
2025-03-14T04:33:12.2671883Z         # when sum over only part of sparse_dims, return a sparse tensor
2025-03-14T04:33:12.2672325Z         >>> torch.sparse.sum(S, [1, 3])
2025-03-14T04:33:12.2672674Z         tensor(indices=tensor([[0, 2, 3]]),
2025-03-14T04:33:12.2673034Z                values=tensor([[-1.4512,  0.4073],
2025-03-14T04:33:12.2673389Z                               [-0.8901,  0.2017],
2025-03-14T04:33:12.2673732Z                               [-0.3183, -1.7539]]),
2025-03-14T04:33:12.2674112Z                size=(5, 2), nnz=3, layout=torch.sparse_coo)
2025-03-14T04:33:12.2674379Z 
2025-03-14T04:33:12.2674540Z         # when sum over all sparse dim, return a dense tensor
2025-03-14T04:33:12.2674932Z         # with summed dims squeezed
2025-03-14T04:33:12.2675273Z         >>> torch.sparse.sum(S, [0, 1, 3])
2025-03-14T04:33:12.2675614Z         tensor([-2.6596, -1.1450])
2025-03-14T04:33:12.2675909Z     
2025-03-14T04:33:12.2676296Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.2676668Z 
2025-03-14T04:33:12.8520620Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39.
2025-03-14T04:33:12.8521761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:12.8522268Z 
2025-03-14T04:33:12.8522513Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2025-03-14T04:33:12.8523071Z maps ``func`` over some dimension of the inputs. Semantically, vmap
2025-03-14T04:33:12.8523623Z pushes the map into PyTorch operations called by ``func``, effectively
2025-03-14T04:33:12.8524087Z vectorizing those operations.
2025-03-14T04:33:12.8524285Z 
2025-03-14T04:33:12.8524522Z vmap is useful for handling batch dimensions: one can write a function
2025-03-14T04:33:12.8525064Z ``func`` that runs on examples and then lift it to a function that can
2025-03-14T04:33:12.8525604Z take batches of examples with ``vmap(func)``. vmap can also be used to
2025-03-14T04:33:12.8526114Z compute batched gradients when composed with autograd.
2025-03-14T04:33:12.8526392Z 
2025-03-14T04:33:12.8526525Z .. note::
2025-03-14T04:33:12.8526837Z     :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2025-03-14T04:33:12.8527288Z     convenience. Use whichever one you'd like.
2025-03-14T04:33:12.8527544Z 
2025-03-14T04:33:12.8527635Z Args:
2025-03-14T04:33:12.8528046Z     func (function): A Python function that takes one or more arguments.
2025-03-14T04:33:12.8528504Z         Must return one or more Tensors.
2025-03-14T04:33:12.8528943Z     in_dims (int or nested structure): Specifies which dimension of the
2025-03-14T04:33:12.8529449Z         inputs should be mapped over. ``in_dims`` should have a
2025-03-14T04:33:12.8529946Z         structure like the inputs. If the ``in_dim`` for a particular
2025-03-14T04:33:12.8530461Z         input is None, then that indicates there is no map dimension.
2025-03-14T04:33:12.8530876Z         Default: 0.
2025-03-14T04:33:12.8531241Z     out_dims (int or Tuple[int]): Specifies where the mapped dimension
2025-03-14T04:33:12.8531765Z         should appear in the outputs. If ``out_dims`` is a Tuple, then
2025-03-14T04:33:12.8532245Z         it should have one element per output. Default: 0.
2025-03-14T04:33:12.8532716Z     randomness (str): Specifies whether the randomness in this
2025-03-14T04:33:12.8533242Z         vmap should be the same or different across batches. If 'different',
2025-03-14T04:33:12.8533790Z         the randomness for each batch will be different. If 'same', the
2025-03-14T04:33:12.8534342Z         randomness will be the same across batches. If 'error', any calls to
2025-03-14T04:33:12.8534952Z         random functions will error. Default: 'error'. WARNING: this flag
2025-03-14T04:33:12.8535503Z         only applies to random PyTorch operations and does not apply to
2025-03-14T04:33:12.8535981Z         Python's random module or numpy randomness.
2025-03-14T04:33:12.8536478Z     chunk_size (None or int): If None (default), apply a single vmap over inputs.
2025-03-14T04:33:12.8537059Z         If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2025-03-14T04:33:12.8537674Z         Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2025-03-14T04:33:12.8538337Z         If you run into memory issues computing the vmap, please try a non-None chunk_size.
2025-03-14T04:33:12.8538724Z 
2025-03-14T04:33:12.8538834Z Returns:
2025-03-14T04:33:12.8539169Z     Returns a new "batched" function. It takes the same inputs as
2025-03-14T04:33:12.8539659Z     ``func``, except each input has an extra dimension at the index
2025-03-14T04:33:12.8540165Z     specified by ``in_dims``. It takes returns the same outputs as
2025-03-14T04:33:12.8540675Z     ``func``, except each output has an extra dimension at the index
2025-03-14T04:33:12.8541097Z     specified by ``out_dims``.
2025-03-14T04:33:12.8541307Z 
2025-03-14T04:33:12.8541404Z .. warning:
2025-03-14T04:33:12.8541750Z     :func:`vmap` works best with functional-style code. Please do not
2025-03-14T04:33:12.8542272Z     perform any side-effects in ``func``, with the exception of
2025-03-14T04:33:12.8542825Z     in-place PyTorch operations. Examples of side-effects include mutating
2025-03-14T04:33:12.8543490Z     Python data structures and assigning values to variables not captured
2025-03-14T04:33:12.8543939Z     in ``func``.
2025-03-14T04:33:12.8544102Z 
2025-03-14T04:33:12.8544340Z One example of using :func:`vmap` is to compute batched dot products. PyTorch
2025-03-14T04:33:12.8544922Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2025-03-14T04:33:12.8545483Z rummaging through docs, use :func:`vmap` to construct a new function.
2025-03-14T04:33:12.8545832Z 
2025-03-14T04:33:12.8546002Z     >>> torch.dot                            # [D], [D] -> []
2025-03-14T04:33:12.8546484Z     >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2025-03-14T04:33:12.8546943Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-14T04:33:12.8547296Z     >>> batched_dot(x, y)
2025-03-14T04:33:12.8547470Z 
2025-03-14T04:33:12.8547716Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2025-03-14T04:33:12.8548184Z model authoring experience.
2025-03-14T04:33:12.8548375Z 
2025-03-14T04:33:12.8548509Z     >>> batch_size, feature_size = 3, 5
2025-03-14T04:33:12.8549131Z     >>> weights = torch.randn(feature_size, requires_grad=True)
2025-03-14T04:33:12.8549589Z     >>>
2025-03-14T04:33:12.8549830Z     >>> def model(feature_vec):
2025-03-14T04:33:12.8550172Z     >>>     # Very simple linear model with activation
2025-03-14T04:33:12.8550571Z     >>>     return feature_vec.dot(weights).relu()
2025-03-14T04:33:12.8550901Z     >>>
2025-03-14T04:33:12.8551202Z     >>> examples = torch.randn(batch_size, feature_size)
2025-03-14T04:33:12.8551603Z     >>> result = torch.vmap(model)(examples)
2025-03-14T04:33:12.8551849Z 
2025-03-14T04:33:12.8552106Z :func:`vmap` can also help vectorize computations that were previously difficult
2025-03-14T04:33:12.8552720Z or impossible to batch. One example is higher-order gradient computation.
2025-03-14T04:33:12.8553312Z The PyTorch autograd engine computes vjps (vector-Jacobian products).
2025-03-14T04:33:12.8553903Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2025-03-14T04:33:12.8554510Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2025-03-14T04:33:12.8555124Z we can vectorize the whole computation, computing the Jacobian in a single
2025-03-14T04:33:12.8555600Z call to ``autograd.grad``.
2025-03-14T04:33:12.8555796Z 
2025-03-14T04:33:12.8555890Z     >>> # Setup
2025-03-14T04:33:12.8556136Z     >>> N = 5
2025-03-14T04:33:12.8556443Z     >>> f = lambda x: x ** 2
2025-03-14T04:33:12.8556768Z     >>> x = torch.randn(N, requires_grad=True)
2025-03-14T04:33:12.8557111Z     >>> y = f(x)
2025-03-14T04:33:12.8557372Z     >>> I_N = torch.eye(N)
2025-03-14T04:33:12.8557652Z     >>>
2025-03-14T04:33:12.8557900Z     >>> # Sequential approach
2025-03-14T04:33:12.8558328Z     >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2025-03-14T04:33:12.8558790Z     >>>                  for v in I_N.unbind()]
2025-03-14T04:33:12.8559209Z     >>> jacobian = torch.stack(jacobian_rows)
2025-03-14T04:33:12.8559551Z     >>>
2025-03-14T04:33:12.8559809Z     >>> # vectorized gradient computation
2025-03-14T04:33:12.8560155Z     >>> def get_vjp(v):
2025-03-14T04:33:12.8560462Z     >>>     return torch.autograd.grad(y, x, v)
2025-03-14T04:33:12.8560834Z     >>> jacobian = torch.vmap(get_vjp)(I_N)
2025-03-14T04:33:12.8561083Z 
2025-03-14T04:33:12.8561353Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2025-03-14T04:33:12.8561754Z 
2025-03-14T04:33:12.8561904Z     >>> torch.dot                            # [D], [D] -> []
2025-03-14T04:33:12.8562446Z     >>> batched_dot = torch.vmap(torch.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2025-03-14T04:33:12.8562986Z     >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2025-03-14T04:33:12.8563374Z     >>> batched_dot(x, y) # tensor of size [2, 3]
2025-03-14T04:33:12.8563630Z 
2025-03-14T04:33:12.8563930Z If the inputs are not batched along the first dimension, ``in_dims`` specifies
2025-03-14T04:33:12.8564489Z the dimension that each inputs are batched along as
2025-03-14T04:33:12.8564769Z 
2025-03-14T04:33:12.8564916Z     >>> torch.dot                            # [N], [N] -> []
2025-03-14T04:33:12.8565410Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2025-03-14T04:33:12.8565886Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-14T04:33:12.8566388Z     >>> batched_dot(x, y)   # output is [5] instead of [2] if batched along the 0th dimension
2025-03-14T04:33:12.8566745Z 
2025-03-14T04:33:12.8567021Z If there are multiple inputs each of which is batched along different dimensions,
2025-03-14T04:33:12.8567612Z ``in_dims`` must be a tuple with the batch dimension for each input as
2025-03-14T04:33:12.8567926Z 
2025-03-14T04:33:12.8568089Z     >>> torch.dot                            # [D], [D] -> []
2025-03-14T04:33:12.8568592Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2025-03-14T04:33:12.8569088Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-14T04:33:12.8569581Z     >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None
2025-03-14T04:33:12.8569975Z 
2025-03-14T04:33:12.8570231Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2025-03-14T04:33:12.8570718Z matching the shape of the input:
2025-03-14T04:33:12.8570924Z 
2025-03-14T04:33:12.8571087Z     >>> f = lambda dict: torch.dot(dict['x'], dict['y'])
2025-03-14T04:33:12.8571475Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-14T04:33:12.8571823Z     >>> input = {'x': x, 'y': y}
2025-03-14T04:33:12.8572208Z     >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
2025-03-14T04:33:12.8572617Z     >>> batched_dot(input)
2025-03-14T04:33:12.8572796Z 
2025-03-14T04:33:12.8573086Z By default, the output is batched along the first dimension. However, it can be batched
2025-03-14T04:33:12.8573628Z along any dimension by using ``out_dims``
2025-03-14T04:33:12.8573868Z 
2025-03-14T04:33:12.8573988Z     >>> f = lambda x: x ** 2
2025-03-14T04:33:12.8574293Z     >>> x = torch.randn(2, 5)
2025-03-14T04:33:12.8574630Z     >>> batched_pow = torch.vmap(f, out_dims=1)
2025-03-14T04:33:12.8574990Z     >>> batched_pow(x) # [5, 2]
2025-03-14T04:33:12.8575180Z 
2025-03-14T04:33:12.8575487Z For any function that uses kwargs, the returned function will not batch the kwargs but will
2025-03-14T04:33:12.8576007Z accept kwargs
2025-03-14T04:33:12.8576155Z 
2025-03-14T04:33:12.8576317Z     >>> x = torch.randn([2, 5])
2025-03-14T04:33:12.8576627Z     >>> def fn(x, scale=4.):
2025-03-14T04:33:12.8576928Z     >>>   return x * scale
2025-03-14T04:33:12.8577192Z     >>>
2025-03-14T04:33:12.8577445Z     >>> batched_pow = torch.vmap(fn)
2025-03-14T04:33:12.8577821Z     >>> assert torch.allclose(batched_pow(x), x * 4)
2025-03-14T04:33:12.8578320Z     >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]
2025-03-14T04:33:12.8578683Z 
2025-03-14T04:33:12.8578788Z .. note::
2025-03-14T04:33:12.8579160Z     vmap does not provide general autobatching or handle variable-length
2025-03-14T04:33:12.8579623Z     sequences out of the box.
2025-03-14T04:33:12.8579832Z 
2025-03-14T04:33:12.8580089Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:12.8580476Z 
2025-03-14T04:33:14.2885169Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=21.
2025-03-14T04:33:14.2886095Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.2886744Z Create a custom operator whose implementation is backed by 1+ triton kernels.
2025-03-14T04:33:14.2887135Z 
2025-03-14T04:33:14.2887349Z     This is a more structured way of using triton kernels with PyTorch.
2025-03-14T04:33:14.2887941Z     Prefer using triton kernels with no ``torch.library`` custom operator wrappers
2025-03-14T04:33:14.2888733Z     (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because
2025-03-14T04:33:14.2889285Z     that is simpler;
2025-03-14T04:33:14.2889711Z     only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you
2025-03-14T04:33:14.2890339Z     want to create an operator that behaves like PyTorch built-in operators.
2025-03-14T04:33:14.2890912Z     For example, you may use a ``torch.library`` wrapper API to define the
2025-03-14T04:33:14.2891479Z     behavior of the triton kernel when passed a tensor subclass or under
2025-03-14T04:33:14.2891935Z     a TorchDispatchMode.
2025-03-14T04:33:14.2892113Z 
2025-03-14T04:33:14.2892385Z     Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op`
2025-03-14T04:33:14.2892872Z     when the implementation
2025-03-14T04:33:14.2893290Z     consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2025-03-14T04:33:14.2893809Z     custom operators as opaque (:func:`torch.compile` and
2025-03-14T04:33:14.2894339Z     :func:`torch.export.export` will never trace into them), but ``triton_op``
2025-03-14T04:33:14.2894930Z     makes the implementation visible to these subsystems, allowing them
2025-03-14T04:33:14.2895481Z     to optimize the triton kernel(s).
2025-03-14T04:33:14.2895698Z 
2025-03-14T04:33:14.2895909Z     Note that ``fn`` must only consist of calls to PyTorch-understood
2025-03-14T04:33:14.2896455Z     operators and triton kernels. Any triton kernels called inside ``fn``
2025-03-14T04:33:14.2897009Z     must be wrapped in a call to :func:`torch.library.wrap_triton`.
2025-03-14T04:33:14.2897317Z 
2025-03-14T04:33:14.2897411Z     Args:
2025-03-14T04:33:14.2897803Z         name (str): A name for the custom op that looks like "{namespace}::{name}",
2025-03-14T04:33:14.2898379Z             e.g. "mylib::my_linear". The name is used as the op's stable identifier
2025-03-14T04:33:14.2898897Z             in PyTorch subsystems (e.g. torch.export, FX graphs).
2025-03-14T04:33:14.2899435Z             To avoid name collisions, please use your project name as the namespace;
2025-03-14T04:33:14.2900015Z             e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2025-03-14T04:33:14.2900627Z         mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2025-03-14T04:33:14.2901269Z             This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2025-03-14T04:33:14.2901899Z             it pessimistically assumes that all inputs to the operator are being mutated.
2025-03-14T04:33:14.2902534Z         schema (None | str): A schema string for the operator. If None
2025-03-14T04:33:14.2903067Z             (recommended) we'll infer a schema for the operator from its type
2025-03-14T04:33:14.2903650Z             annotations. We recommend letting us infer a schema unless you
2025-03-14T04:33:14.2904113Z             have a specific reason not to.
2025-03-14T04:33:14.2904513Z             Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2025-03-14T04:33:14.2904793Z 
2025-03-14T04:33:14.2904899Z     Example::
2025-03-14T04:33:14.2905054Z 
2025-03-14T04:33:14.2905204Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:14.2905569Z         >>> import torch
2025-03-14T04:33:14.2905905Z         >>> from torch.library import triton_op, wrap_triton
2025-03-14T04:33:14.2906274Z         >>>
2025-03-14T04:33:14.2906515Z         >>> import triton
2025-03-14T04:33:14.2906822Z         >>> from triton import language as tl
2025-03-14T04:33:14.2907152Z         >>>
2025-03-14T04:33:14.2907389Z         >>> @triton.jit
2025-03-14T04:33:14.2907658Z         >>> def add_kernel(
2025-03-14T04:33:14.2907941Z         >>>     in_ptr0,
2025-03-14T04:33:14.2908213Z         >>>     in_ptr1,
2025-03-14T04:33:14.2908481Z         >>>     out_ptr,
2025-03-14T04:33:14.2908752Z         >>>     n_elements,
2025-03-14T04:33:14.2909055Z         >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-14T04:33:14.2909366Z         >>> ):
2025-03-14T04:33:14.2909632Z         >>>     pid = tl.program_id(axis=0)
2025-03-14T04:33:14.2910053Z         >>>     block_start = pid * BLOCK_SIZE
2025-03-14T04:33:14.2910444Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-14T04:33:14.2910839Z         >>>     mask = offsets < n_elements
2025-03-14T04:33:14.2911208Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-14T04:33:14.2911597Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-14T04:33:14.2921006Z         >>>     output = x + y
2025-03-14T04:33:14.2921525Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-14T04:33:14.2921909Z         >>>
2025-03-14T04:33:14.2922215Z         >>> @triton_op("mylib::add", mutates_args={})
2025-03-14T04:33:14.2922674Z         >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2025-03-14T04:33:14.2923116Z         >>>     output = torch.empty_like(x)
2025-03-14T04:33:14.2923469Z         >>>     n_elements = output.numel()
2025-03-14T04:33:14.2923807Z         >>>
2025-03-14T04:33:14.2924059Z         >>>     def grid(meta):
2025-03-14T04:33:14.2924447Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-14T04:33:14.2924845Z         >>>
2025-03-14T04:33:14.2925277Z         >>>     # NB: we need to wrap the triton kernel in a call to wrap_triton
2025-03-14T04:33:14.2925790Z         >>>     wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2025-03-14T04:33:14.2926213Z         >>>     return output
2025-03-14T04:33:14.2926509Z         >>>
2025-03-14T04:33:14.2926755Z         >>> @torch.compile
2025-03-14T04:33:14.2927049Z         >>> def f(x, y):
2025-03-14T04:33:14.2927336Z         >>>     return add(x, y)
2025-03-14T04:33:14.2927632Z         >>>
2025-03-14T04:33:14.2927895Z         >>> x = torch.randn(3, device="cuda")
2025-03-14T04:33:14.2928256Z         >>> y = torch.randn(3, device="cuda")
2025-03-14T04:33:14.2928583Z         >>>
2025-03-14T04:33:14.2928799Z         >>> z = f(x, y)
2025-03-14T04:33:14.2929092Z         >>> assert torch.allclose(z, x + y)
2025-03-14T04:33:14.2929337Z 
2025-03-14T04:33:14.2929432Z     
2025-03-14T04:33:14.2929818Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.2930204Z 
2025-03-14T04:33:14.2930715Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=202.
2025-03-14T04:33:14.2931598Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.2932222Z Allows capture of a triton kernel into a graph via make_fx or
2025-03-14T04:33:14.2932656Z     non-strict ``torch.export``.
2025-03-14T04:33:14.2932872Z 
2025-03-14T04:33:14.2933064Z     These technologies perform Dispatcher-based tracing (via
2025-03-14T04:33:14.2933575Z     ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2025-03-14T04:33:14.2934108Z     The ``wrap_triton`` API wraps a triton kernel into a callable that
2025-03-14T04:33:14.2934557Z     can actually be traced into a graph.
2025-03-14T04:33:14.2934804Z 
2025-03-14T04:33:14.2935023Z     Please use this API together with :func:`torch.library.triton_op`.
2025-03-14T04:33:14.2935365Z 
2025-03-14T04:33:14.2935467Z     Examples:
2025-03-14T04:33:14.2935620Z 
2025-03-14T04:33:14.2935730Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:14.2936038Z         >>> import torch
2025-03-14T04:33:14.2936326Z         >>> import triton
2025-03-14T04:33:14.2936639Z         >>> from triton import language as tl
2025-03-14T04:33:14.2937074Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-14T04:33:14.2937518Z         >>> from torch.library import wrap_triton
2025-03-14T04:33:14.2937861Z         >>>
2025-03-14T04:33:14.2938100Z         >>> @triton.jit
2025-03-14T04:33:14.2938375Z         >>> def add_kernel(
2025-03-14T04:33:14.2938661Z         >>>     in_ptr0,
2025-03-14T04:33:14.2938923Z         >>>     in_ptr1,
2025-03-14T04:33:14.2939194Z         >>>     out_ptr,
2025-03-14T04:33:14.2939468Z         >>>     n_elements,
2025-03-14T04:33:14.2939859Z         >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-14T04:33:14.2940192Z         >>> ):
2025-03-14T04:33:14.2940466Z         >>>     pid = tl.program_id(axis=0)
2025-03-14T04:33:14.2940827Z         >>>     block_start = pid * BLOCK_SIZE
2025-03-14T04:33:14.2941207Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-14T04:33:14.2941598Z         >>>     mask = offsets < n_elements
2025-03-14T04:33:14.2941966Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-14T04:33:14.2942360Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-14T04:33:14.2942726Z         >>>     output = x + y
2025-03-14T04:33:14.2943075Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-14T04:33:14.2943441Z         >>>
2025-03-14T04:33:14.2943685Z         >>> def add(x, y):
2025-03-14T04:33:14.2943992Z         >>>     output = torch.empty_like(x)
2025-03-14T04:33:14.2944336Z         >>>     n_elements = output.numel()
2025-03-14T04:33:14.2944664Z         >>>
2025-03-14T04:33:14.2944913Z         >>>     def grid_fn(meta):
2025-03-14T04:33:14.2945305Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-14T04:33:14.2945730Z         >>>
2025-03-14T04:33:14.2946067Z         >>>     wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2025-03-14T04:33:14.2946497Z         >>>     return output
2025-03-14T04:33:14.2946783Z         >>>
2025-03-14T04:33:14.2947046Z         >>> x = torch.randn(3, device="cuda")
2025-03-14T04:33:14.2947411Z         >>> y = torch.randn(3, device="cuda")
2025-03-14T04:33:14.2947759Z         >>> gm = make_fx(add)(x, y)
2025-03-14T04:33:14.2948081Z         >>> print(gm.code)
2025-03-14T04:33:14.2948387Z         >>> # def forward(self, x_1, y_1):
2025-03-14T04:33:14.2949106Z         >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2025-03-14T04:33:14.2949729Z         >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2025-03-14T04:33:14.2950241Z         >>> #         kernel_idx = 0, constant_args_idx = 0,
2025-03-14T04:33:14.2950622Z         >>> #         grid = [(1, 1, 1)], kwargs = {
2025-03-14T04:33:14.2951030Z         >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2025-03-14T04:33:14.2951450Z         >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2025-03-14T04:33:14.2951803Z         >>> #         })
2025-03-14T04:33:14.2952073Z         >>> #     return empty_like
2025-03-14T04:33:14.2952296Z 
2025-03-14T04:33:14.2952468Z     
2025-03-14T04:33:14.2952862Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.2953250Z 
2025-03-14T04:33:14.3618728Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=326.
2025-03-14T04:33:14.3619677Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.3620161Z 
2025-03-14T04:33:14.3620384Z Raises an AssertionError if two items are not equal up to desired
2025-03-14T04:33:14.3620829Z precision.
2025-03-14T04:33:14.3620979Z 
2025-03-14T04:33:14.3621177Z .. note:: It is recommended to use one of `assert_allclose`,
2025-03-14T04:33:14.3621664Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-14T04:33:14.3622168Z           instead of this function for more consistent floating point
2025-03-14T04:33:14.3622583Z           comparisons.
2025-03-14T04:33:14.3622767Z 
2025-03-14T04:33:14.3622990Z The test verifies that the elements of `actual` and `desired` satisfy.
2025-03-14T04:33:14.3623339Z 
2025-03-14T04:33:14.3623505Z     ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2025-03-14T04:33:14.3623794Z 
2025-03-14T04:33:14.3624030Z That is a looser test than originally documented, but agrees with what the
2025-03-14T04:33:14.3624720Z actual implementation in `assert_array_almost_equal` did up to rounding
2025-03-14T04:33:14.3625323Z vagaries. An exception is raised at conflicting values. For ndarrays this
2025-03-14T04:33:14.3626002Z delegates to assert_array_almost_equal
2025-03-14T04:33:14.3626247Z 
2025-03-14T04:33:14.3626345Z Parameters
2025-03-14T04:33:14.3626589Z ----------
2025-03-14T04:33:14.3626837Z actual : array_like
2025-03-14T04:33:14.3627109Z     The object to check.
2025-03-14T04:33:14.3627399Z desired : array_like
2025-03-14T04:33:14.3627673Z     The expected object.
2025-03-14T04:33:14.3627965Z decimal : int, optional
2025-03-14T04:33:14.3628335Z     Desired precision, default is 7.
2025-03-14T04:33:14.3628671Z err_msg : str, optional
2025-03-14T04:33:14.3629098Z     The error message to be printed in case of failure.
2025-03-14T04:33:14.3629489Z verbose : bool, optional
2025-03-14T04:33:14.3629864Z     If True, the conflicting values are appended to the error message.
2025-03-14T04:33:14.3630201Z 
2025-03-14T04:33:14.3630291Z Raises
2025-03-14T04:33:14.3630521Z ------
2025-03-14T04:33:14.3630756Z AssertionError
2025-03-14T04:33:14.3631116Z   If actual and desired are not equal up to specified precision.
2025-03-14T04:33:14.3631445Z 
2025-03-14T04:33:14.3631538Z See Also
2025-03-14T04:33:14.3631767Z --------
2025-03-14T04:33:14.3632207Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:14.3632709Z                  relative and/or absolute precision.
2025-03-14T04:33:14.3633180Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:14.3633502Z 
2025-03-14T04:33:14.3633614Z Examples
2025-03-14T04:33:14.3633852Z --------
2025-03-14T04:33:14.3634160Z >>> from torch._numpy.testing import assert_almost_equal
2025-03-14T04:33:14.3634590Z >>> assert_almost_equal(2.3333333333333, 2.33333334)
2025-03-14T04:33:14.3635028Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2025-03-14T04:33:14.3635448Z Traceback (most recent call last):
2025-03-14T04:33:14.3635761Z     ...
2025-03-14T04:33:14.3635996Z AssertionError:
2025-03-14T04:33:14.3636271Z Arrays are not almost equal to 10 decimals
2025-03-14T04:33:14.3636626Z  ACTUAL: 2.3333333333333
2025-03-14T04:33:14.3636905Z  DESIRED: 2.33333334
2025-03-14T04:33:14.3637077Z 
2025-03-14T04:33:14.3637235Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
2025-03-14T04:33:14.3637642Z ...                     np.array([1.0,2.33333334]), decimal=9)
2025-03-14T04:33:14.3638016Z Traceback (most recent call last):
2025-03-14T04:33:14.3638329Z     ...
2025-03-14T04:33:14.3638570Z AssertionError:
2025-03-14T04:33:14.3638851Z Arrays are not almost equal to 9 decimals
2025-03-14T04:33:14.3639333Z <BLANKLINE>
2025-03-14T04:33:14.3639598Z Mismatched elements: 1 / 2 (50%)
2025-03-14T04:33:14.3639946Z Max absolute difference: 6.666699636781459e-09
2025-03-14T04:33:14.3640333Z Max relative difference: 2.8571569790287484e-09
2025-03-14T04:33:14.3640734Z  x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-14T04:33:14.3641140Z  y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-14T04:33:14.3641389Z 
2025-03-14T04:33:14.3641393Z 
2025-03-14T04:33:14.3641671Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.3642045Z 
2025-03-14T04:33:14.3642625Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=451.
2025-03-14T04:33:14.3643569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.3643946Z 
2025-03-14T04:33:14.3644188Z Raises an AssertionError if two items are not equal up to significant
2025-03-14T04:33:14.3644632Z digits.
2025-03-14T04:33:14.3644758Z 
2025-03-14T04:33:14.3644967Z .. note:: It is recommended to use one of `assert_allclose`,
2025-03-14T04:33:14.3645448Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-14T04:33:14.3645946Z           instead of this function for more consistent floating point
2025-03-14T04:33:14.3646358Z           comparisons.
2025-03-14T04:33:14.3646519Z 
2025-03-14T04:33:14.3646711Z Given two numbers, check that they are approximately equal.
2025-03-14T04:33:14.3647327Z Approximately equal is defined as the number of significant digits
2025-03-14T04:33:14.3647764Z that agree.
2025-03-14T04:33:14.3647914Z 
2025-03-14T04:33:14.3648009Z Parameters
2025-03-14T04:33:14.3648244Z ----------
2025-03-14T04:33:14.3648490Z actual : scalar
2025-03-14T04:33:14.3648940Z     The object to check.
2025-03-14T04:33:14.3649239Z desired : scalar
2025-03-14T04:33:14.3649508Z     The expected object.
2025-03-14T04:33:14.3649814Z significant : int, optional
2025-03-14T04:33:14.3650136Z     Desired precision, default is 7.
2025-03-14T04:33:14.3650474Z err_msg : str, optional
2025-03-14T04:33:14.3650819Z     The error message to be printed in case of failure.
2025-03-14T04:33:14.3651197Z verbose : bool, optional
2025-03-14T04:33:14.3651574Z     If True, the conflicting values are appended to the error message.
2025-03-14T04:33:14.3651909Z 
2025-03-14T04:33:14.3651998Z Raises
2025-03-14T04:33:14.3652218Z ------
2025-03-14T04:33:14.3652452Z AssertionError
2025-03-14T04:33:14.3652821Z   If actual and desired are not equal up to specified precision.
2025-03-14T04:33:14.3653144Z 
2025-03-14T04:33:14.3653237Z See Also
2025-03-14T04:33:14.3653547Z --------
2025-03-14T04:33:14.3653931Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:14.3654436Z                  relative and/or absolute precision.
2025-03-14T04:33:14.3654907Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:14.3655248Z 
2025-03-14T04:33:14.3655345Z Examples
2025-03-14T04:33:14.3655580Z --------
2025-03-14T04:33:14.3655994Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)  # doctest: +SKIP
2025-03-14T04:33:14.3656654Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,  # doctest: +SKIP
2025-03-14T04:33:14.3657165Z ...                                significant=8)
2025-03-14T04:33:14.3657665Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,  # doctest: +SKIP
2025-03-14T04:33:14.3658177Z ...                                significant=8)
2025-03-14T04:33:14.3658538Z Traceback (most recent call last):
2025-03-14T04:33:14.3658850Z     ...
2025-03-14T04:33:14.3659092Z AssertionError:
2025-03-14T04:33:14.3659393Z Items are not equal to 8 significant digits:
2025-03-14T04:33:14.3659747Z  ACTUAL: 1.234567e-21
2025-03-14T04:33:14.3660007Z  DESIRED: 1.2345672e-21
2025-03-14T04:33:14.3660189Z 
2025-03-14T04:33:14.3660353Z the evaluated condition that raises the exception is
2025-03-14T04:33:14.3660681Z 
2025-03-14T04:33:14.3660861Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
2025-03-14T04:33:14.3661241Z True
2025-03-14T04:33:14.3661373Z 
2025-03-14T04:33:14.3661377Z 
2025-03-14T04:33:14.3661632Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.3662017Z 
2025-03-14T04:33:14.3662569Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=730.
2025-03-14T04:33:14.3663505Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.3663894Z 
2025-03-14T04:33:14.3664108Z Raises an AssertionError if two array_like objects are not equal.
2025-03-14T04:33:14.3664442Z 
2025-03-14T04:33:14.3664652Z Given two array_like objects, check that the shape is equal and all
2025-03-14T04:33:14.3665203Z elements of these objects are equal (but see the Notes for the special
2025-03-14T04:33:14.3665753Z handling of a scalar). An exception is raised at shape mismatch or
2025-03-14T04:33:14.3666302Z conflicting values. In contrast to the standard usage in numpy, NaNs
2025-03-14T04:33:14.3666869Z are compared like numbers, no assertion is raised if both objects have
2025-03-14T04:33:14.3667322Z NaNs in the same positions.
2025-03-14T04:33:14.3667526Z 
2025-03-14T04:33:14.3667753Z The usual caution for verifying equality with floating point numbers is
2025-03-14T04:33:14.3668202Z advised.
2025-03-14T04:33:14.3668431Z 
2025-03-14T04:33:14.3668571Z Parameters
2025-03-14T04:33:14.3668807Z ----------
2025-03-14T04:33:14.3669044Z x : array_like
2025-03-14T04:33:14.3669306Z     The actual object to check.
2025-03-14T04:33:14.3669612Z y : array_like
2025-03-14T04:33:14.3669879Z     The desired, expected object.
2025-03-14T04:33:14.3670196Z err_msg : str, optional
2025-03-14T04:33:14.3670527Z     The error message to be printed in case of failure.
2025-03-14T04:33:14.3670907Z verbose : bool, optional
2025-03-14T04:33:14.3671303Z     If True, the conflicting values are appended to the error message.
2025-03-14T04:33:14.3671721Z strict : bool, optional
2025-03-14T04:33:14.3672103Z     If True, raise an AssertionError when either the shape or the data
2025-03-14T04:33:14.3672605Z     type of the array_like objects does not match. The special
2025-03-14T04:33:14.3673111Z     handling for scalars mentioned in the Notes section is disabled.
2025-03-14T04:33:14.3673438Z 
2025-03-14T04:33:14.3673527Z Raises
2025-03-14T04:33:14.3673752Z ------
2025-03-14T04:33:14.3673989Z AssertionError
2025-03-14T04:33:14.3674280Z     If actual and desired objects are not equal.
2025-03-14T04:33:14.3674538Z 
2025-03-14T04:33:14.3674667Z See Also
2025-03-14T04:33:14.3674890Z --------
2025-03-14T04:33:14.3675263Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:14.3675764Z                  relative and/or absolute precision.
2025-03-14T04:33:14.3676225Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:14.3676547Z 
2025-03-14T04:33:14.3676651Z Notes
2025-03-14T04:33:14.3676873Z -----
2025-03-14T04:33:14.3677196Z When one of `x` and `y` is a scalar and the other is array_like, the
2025-03-14T04:33:14.3677765Z function checks that each element of the array_like object is equal to
2025-03-14T04:33:14.3678374Z the scalar. This behaviour can be disabled with the `strict` parameter.
2025-03-14T04:33:14.3678714Z 
2025-03-14T04:33:14.3678821Z Examples
2025-03-14T04:33:14.3679045Z --------
2025-03-14T04:33:14.3679369Z The first assert does not raise an exception:
2025-03-14T04:33:14.3679631Z 
2025-03-14T04:33:14.3679789Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
2025-03-14T04:33:14.3680202Z ...                               [np.exp(0),2.33333, np.nan])
2025-03-14T04:33:14.3680456Z 
2025-03-14T04:33:14.3680687Z Use `assert_allclose` or one of the nulp (number of floating point values)
2025-03-14T04:33:14.3681159Z functions for these cases instead:
2025-03-14T04:33:14.3681381Z 
2025-03-14T04:33:14.3681570Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
2025-03-14T04:33:14.3681971Z ...                            [1, np.sqrt(np.pi)**2, np.nan],
2025-03-14T04:33:14.3682345Z ...                            rtol=1e-10, atol=0)
2025-03-14T04:33:14.3682585Z 
2025-03-14T04:33:14.3682794Z As mentioned in the Notes section, `assert_array_equal` has special
2025-03-14T04:33:14.3683350Z handling for scalars. Here the test checks that each value in `x` is 3:
2025-03-14T04:33:14.3683705Z 
2025-03-14T04:33:14.3683825Z >>> x = np.full((2, 5), fill_value=3)
2025-03-14T04:33:14.3684170Z >>> np.testing.assert_array_equal(x, 3)
2025-03-14T04:33:14.3684409Z 
2025-03-14T04:33:14.3684633Z Use `strict` to raise an AssertionError when comparing a scalar with an
2025-03-14T04:33:14.3685066Z array:
2025-03-14T04:33:14.3685205Z 
2025-03-14T04:33:14.3685361Z >>> np.testing.assert_array_equal(x, 3, strict=True)
2025-03-14T04:33:14.3685754Z Traceback (most recent call last):
2025-03-14T04:33:14.3686061Z     ...
2025-03-14T04:33:14.3686293Z AssertionError:
2025-03-14T04:33:14.3686551Z Arrays are not equal
2025-03-14T04:33:14.3686816Z <BLANKLINE>
2025-03-14T04:33:14.3687062Z (shapes (2, 5), () mismatch)
2025-03-14T04:33:14.3687369Z  x: torch.ndarray([[3, 3, 3, 3, 3],
2025-03-14T04:33:14.3687687Z         [3, 3, 3, 3, 3]])
2025-03-14T04:33:14.3687950Z  y: torch.ndarray(3)
2025-03-14T04:33:14.3688125Z 
2025-03-14T04:33:14.3688344Z The `strict` parameter also ensures that the array data types match:
2025-03-14T04:33:14.3688721Z 
2025-03-14T04:33:14.3688857Z >>> x = np.array([2, 2, 2])
2025-03-14T04:33:14.3689184Z >>> y = np.array([2., 2., 2.], dtype=np.float32)
2025-03-14T04:33:14.3689603Z >>> np.testing.assert_array_equal(x, y, strict=True)
2025-03-14T04:33:14.3689999Z Traceback (most recent call last):
2025-03-14T04:33:14.3690316Z     ...
2025-03-14T04:33:14.3690556Z AssertionError:
2025-03-14T04:33:14.3690820Z Arrays are not equal
2025-03-14T04:33:14.3691092Z <BLANKLINE>
2025-03-14T04:33:14.3691391Z (dtypes dtype("int64"), dtype("float32") mismatch)
2025-03-14T04:33:14.3691780Z  x: torch.ndarray([2, 2, 2])
2025-03-14T04:33:14.3692093Z  y: torch.ndarray([2., 2., 2.])
2025-03-14T04:33:14.3692288Z 
2025-03-14T04:33:14.3692564Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.3692936Z 
2025-03-14T04:33:14.3693543Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=836.
2025-03-14T04:33:14.3694511Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.3694890Z 
2025-03-14T04:33:14.3695128Z Raises an AssertionError if two objects are not equal up to desired
2025-03-14T04:33:14.3695603Z precision.
2025-03-14T04:33:14.3695734Z 
2025-03-14T04:33:14.3695930Z .. note:: It is recommended to use one of `assert_allclose`,
2025-03-14T04:33:14.3696405Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-14T04:33:14.3696901Z           instead of this function for more consistent floating point
2025-03-14T04:33:14.3697311Z           comparisons.
2025-03-14T04:33:14.3697473Z 
2025-03-14T04:33:14.3697726Z The test verifies identical shapes and that the elements of ``actual`` and
2025-03-14T04:33:14.3698189Z ``desired`` satisfy.
2025-03-14T04:33:14.3698348Z 
2025-03-14T04:33:14.3698499Z     ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2025-03-14T04:33:14.3698743Z 
2025-03-14T04:33:14.3698990Z That is a looser test than originally documented, but agrees with what the
2025-03-14T04:33:14.3699576Z actual implementation did up to rounding vagaries. An exception is raised
2025-03-14T04:33:14.3700181Z at shape mismatch or conflicting values. In contrast to the standard usage
2025-03-14T04:33:14.3700761Z in numpy, NaNs are compared like numbers, no assertion is raised if both
2025-03-14T04:33:14.3701235Z objects have NaNs in the same positions.
2025-03-14T04:33:14.3701475Z 
2025-03-14T04:33:14.3701570Z Parameters
2025-03-14T04:33:14.3701834Z ----------
2025-03-14T04:33:14.3702072Z x : array_like
2025-03-14T04:33:14.3702340Z     The actual object to check.
2025-03-14T04:33:14.3702643Z y : array_like
2025-03-14T04:33:14.3702910Z     The desired, expected object.
2025-03-14T04:33:14.3703232Z decimal : int, optional
2025-03-14T04:33:14.3703528Z     Desired precision, default is 6.
2025-03-14T04:33:14.3703860Z err_msg : str, optional
2025-03-14T04:33:14.3704194Z   The error message to be printed in case of failure.
2025-03-14T04:33:14.3704583Z verbose : bool, optional
2025-03-14T04:33:14.3704975Z     If True, the conflicting values are appended to the error message.
2025-03-14T04:33:14.3705295Z 
2025-03-14T04:33:14.3705402Z Raises
2025-03-14T04:33:14.3705627Z ------
2025-03-14T04:33:14.3705846Z AssertionError
2025-03-14T04:33:14.3706202Z     If actual and desired are not equal up to specified precision.
2025-03-14T04:33:14.3706528Z 
2025-03-14T04:33:14.3706621Z See Also
2025-03-14T04:33:14.3706844Z --------
2025-03-14T04:33:14.3707221Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:14.3707718Z                  relative and/or absolute precision.
2025-03-14T04:33:14.3708183Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:14.3708520Z 
2025-03-14T04:33:14.3708616Z Examples
2025-03-14T04:33:14.3708841Z --------
2025-03-14T04:33:14.3709109Z the first assert does not raise an exception
2025-03-14T04:33:14.3709345Z 
2025-03-14T04:33:14.3709568Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
2025-03-14T04:33:14.3710015Z ...                                      [1.0,2.333,np.nan])
2025-03-14T04:33:14.3710246Z 
2025-03-14T04:33:14.3710447Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-14T04:33:14.3710879Z ...                                      [1.0,2.33339,np.nan], decimal=5)
2025-03-14T04:33:14.3711261Z Traceback (most recent call last):
2025-03-14T04:33:14.3711572Z     ...
2025-03-14T04:33:14.3711807Z AssertionError:
2025-03-14T04:33:14.3712088Z Arrays are not almost equal to 5 decimals
2025-03-14T04:33:14.3712420Z <BLANKLINE>
2025-03-14T04:33:14.3712657Z Mismatched elements: 1 / 3 (33.3%)
2025-03-14T04:33:14.3713004Z Max absolute difference: 5.999999999994898e-05
2025-03-14T04:33:14.3713384Z Max relative difference: 2.5713661239633743e-05
2025-03-14T04:33:14.3713802Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-14T04:33:14.3714253Z  y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2025-03-14T04:33:14.3714540Z 
2025-03-14T04:33:14.3714725Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-14T04:33:14.3715156Z ...                                      [1.0,2.33333, 5], decimal=5)
2025-03-14T04:33:14.3715558Z Traceback (most recent call last):
2025-03-14T04:33:14.3715869Z     ...
2025-03-14T04:33:14.3716104Z AssertionError:
2025-03-14T04:33:14.3716385Z Arrays are not almost equal to 5 decimals
2025-03-14T04:33:14.3716725Z <BLANKLINE>
2025-03-14T04:33:14.3716979Z x and y nan location mismatch:
2025-03-14T04:33:14.3717356Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-14T04:33:14.3717808Z  y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2025-03-14T04:33:14.3718078Z 
2025-03-14T04:33:14.3718082Z 
2025-03-14T04:33:14.3718353Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.3718721Z 
2025-03-14T04:33:14.3719451Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1786.
2025-03-14T04:33:14.3720417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.3721024Z Context manager that resets warning registry for catching warnings
2025-03-14T04:33:14.3721352Z 
2025-03-14T04:33:14.3721615Z     Warnings can be slippery, because, whenever a warning is triggered, Python
2025-03-14T04:33:14.3722219Z     adds a ``__warningregistry__`` member to the *calling* module.  This makes
2025-03-14T04:33:14.3722855Z     it impossible to retrigger the warning in this module, whatever you put in
2025-03-14T04:33:14.3723476Z     the warnings filters.  This context manager accepts a sequence of `modules`
2025-03-14T04:33:14.3723989Z     as a keyword argument to its constructor and:
2025-03-14T04:33:14.3724240Z 
2025-03-14T04:33:14.3724490Z     * stores and removes any ``__warningregistry__`` entries in given `modules`
2025-03-14T04:33:14.3724944Z       on entry;
2025-03-14T04:33:14.3725299Z     * resets ``__warningregistry__`` to its previous state on exit.
2025-03-14T04:33:14.3725606Z 
2025-03-14T04:33:14.3725853Z     This makes it possible to trigger any warning afresh inside the context
2025-03-14T04:33:14.3726403Z     manager without disturbing the state of warnings outside.
2025-03-14T04:33:14.3726703Z 
2025-03-14T04:33:14.3726958Z     For compatibility with Python 3.0, please consider all arguments to be
2025-03-14T04:33:14.3727426Z     keyword-only.
2025-03-14T04:33:14.3727580Z 
2025-03-14T04:33:14.3727697Z     Parameters
2025-03-14T04:33:14.3727954Z     ----------
2025-03-14T04:33:14.3728204Z     record : bool, optional
2025-03-14T04:33:14.3728595Z         Specifies whether warnings should be captured by a custom
2025-03-14T04:33:14.3729159Z         implementation of ``warnings.showwarning()`` and be appended to a list
2025-03-14T04:33:14.3729736Z         returned by the context manager. Otherwise None is returned by the
2025-03-14T04:33:14.3730341Z         context manager. The objects appended to the list are arguments whose
2025-03-14T04:33:14.3730906Z         attributes mirror the arguments to ``showwarning()``.
2025-03-14T04:33:14.3731319Z     modules : sequence, optional
2025-03-14T04:33:14.3731762Z         Sequence of modules for which to reset warnings registry on entry and
2025-03-14T04:33:14.3732312Z         restore on exit. To work correctly, all 'ignore' filters should
2025-03-14T04:33:14.3732752Z         filter by one of these modules.
2025-03-14T04:33:14.3732982Z 
2025-03-14T04:33:14.3733077Z     Examples
2025-03-14T04:33:14.3733316Z     --------
2025-03-14T04:33:14.3733559Z     >>> import warnings
2025-03-14T04:33:14.3733923Z     >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2025-03-14T04:33:14.3734363Z     ...         modules=[np.core.fromnumeric]):
2025-03-14T04:33:14.3734737Z     ...     warnings.simplefilter('always')
2025-03-14T04:33:14.3735202Z     ...     warnings.filterwarnings('ignore', module='np.core.fromnumeric')
2025-03-14T04:33:14.3735731Z     ...     # do something that raises a warning but ignore those in
2025-03-14T04:33:14.3736141Z     ...     # np.core.fromnumeric
2025-03-14T04:33:14.3736440Z     
2025-03-14T04:33:14.3736843Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.3737224Z 
2025-03-14T04:33:14.5255745Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354.
2025-03-14T04:33:14.5256697Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.5257307Z Applies a 1D convolution over a quantized input signal composed of
2025-03-14T04:33:14.5257766Z     several quantized input planes.
2025-03-14T04:33:14.5257982Z 
2025-03-14T04:33:14.5258215Z     For details on input arguments, parameters, and implementation see
2025-03-14T04:33:14.5258652Z     :class:`~torch.nn.Conv1d`.
2025-03-14T04:33:14.5258864Z 
2025-03-14T04:33:14.5258977Z     .. note::
2025-03-14T04:33:14.5259340Z         Only `zeros` is supported for the :attr:`padding_mode` argument.
2025-03-14T04:33:14.5259674Z 
2025-03-14T04:33:14.5259772Z     .. note::
2025-03-14T04:33:14.5260107Z         Only `torch.quint8` is supported for the input data type.
2025-03-14T04:33:14.5260419Z 
2025-03-14T04:33:14.5260423Z 
2025-03-14T04:33:14.5260522Z     Attributes:
2025-03-14T04:33:14.5260894Z         weight (Tensor):     packed tensor derived from the learnable weight
2025-03-14T04:33:14.5261342Z                              parameter.
2025-03-14T04:33:14.5261887Z         scale (Tensor):      scalar for the output scale
2025-03-14T04:33:14.5262332Z         zero_point (Tensor): scalar for the output zero point
2025-03-14T04:33:14.5262628Z 
2025-03-14T04:33:14.5262791Z     See :class:`~torch.nn.Conv1d` for other attributes.
2025-03-14T04:33:14.5263073Z 
2025-03-14T04:33:14.5263184Z     Examples::
2025-03-14T04:33:14.5263341Z 
2025-03-14T04:33:14.5263498Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2025-03-14T04:33:14.5263919Z         >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2025-03-14T04:33:14.5264292Z         >>> input = torch.randn(20, 16, 100)
2025-03-14T04:33:14.5264645Z         >>> # quantize input to quint8
2025-03-14T04:33:14.5264974Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:14.5265385Z         >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2025-03-14T04:33:14.5265859Z         ...                                     dtype=torch.quint8)
2025-03-14T04:33:14.5266221Z         >>> output = m(q_input)
2025-03-14T04:33:14.5266411Z 
2025-03-14T04:33:14.5266519Z     
2025-03-14T04:33:14.5266902Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.5267273Z 
2025-03-14T04:33:14.5439998Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11.
2025-03-14T04:33:14.5440921Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.5441638Z A quantized long short-term memory (LSTM).
2025-03-14T04:33:14.5441875Z 
2025-03-14T04:33:14.5442175Z     For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2025-03-14T04:33:14.5442576Z 
2025-03-14T04:33:14.5442686Z     Attributes:
2025-03-14T04:33:14.5442967Z         layers : instances of the `_LSTMLayer`
2025-03-14T04:33:14.5443200Z 
2025-03-14T04:33:14.5443322Z     .. note::
2025-03-14T04:33:14.5443683Z         To access the weights and biases, you need to access them per layer.
2025-03-14T04:33:14.5444200Z         See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2025-03-14T04:33:14.5444487Z 
2025-03-14T04:33:14.5444598Z     Examples::
2025-03-14T04:33:14.5444855Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:14.5445162Z         >>> custom_module_config = {
2025-03-14T04:33:14.5445529Z         ...     'float_to_observed_custom_module_class': {
2025-03-14T04:33:14.5445907Z         ...         nn.LSTM: nn.quantizable.LSTM,
2025-03-14T04:33:14.5446252Z         ...     },
2025-03-14T04:33:14.5446562Z         ...     'observed_to_quantized_custom_module_class': {
2025-03-14T04:33:14.5446988Z         ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2025-03-14T04:33:14.5447404Z         ...     }
2025-03-14T04:33:14.5447653Z         ... }
2025-03-14T04:33:14.5448027Z         >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2025-03-14T04:33:14.5448597Z         >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2025-03-14T04:33:14.5449252Z     
2025-03-14T04:33:14.5449641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.5450008Z 
2025-03-14T04:33:14.6250676Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227.
2025-03-14T04:33:14.6251759Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.6252331Z Squashes the sparse masks into the appropriate tensors.
2025-03-14T04:33:14.6252638Z 
2025-03-14T04:33:14.6252853Z         If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2025-03-14T04:33:14.6253383Z         the module will have a `sparse_params` dict attached to it.
2025-03-14T04:33:14.6253700Z 
2025-03-14T04:33:14.6253797Z         Args:
2025-03-14T04:33:14.6254141Z             params_to_keep: List of keys to save in the module or a dict
2025-03-14T04:33:14.6254753Z                             representing the modules and keys that will have
2025-03-14T04:33:14.6255200Z                             sparsity parameters saved
2025-03-14T04:33:14.6255677Z             params_to_keep_per_layer: Dict to specify the params that should be
2025-03-14T04:33:14.6256191Z                             saved for specific layers. The keys in the dict
2025-03-14T04:33:14.6256644Z                             should be the module fqn, while the values should
2025-03-14T04:33:14.6257106Z                             be a list of strings with the names of the variables
2025-03-14T04:33:14.6257538Z                             to save in the `sparse_params`
2025-03-14T04:33:14.6257787Z 
2025-03-14T04:33:14.6257904Z         Examples:
2025-03-14T04:33:14.6258209Z             >>> # xdoctest: +SKIP("locals are undefined")
2025-03-14T04:33:14.6258596Z             >>> # Don't save any sparse params
2025-03-14T04:33:14.6258964Z             >>> sparsifier.squash_mask()
2025-03-14T04:33:14.6259348Z             >>> hasattr(model.submodule1, 'sparse_params')
2025-03-14T04:33:14.6259713Z             False
2025-03-14T04:33:14.6259860Z 
2025-03-14T04:33:14.6260002Z             >>> # Keep sparse params per layer
2025-03-14T04:33:14.6260371Z             >>> sparsifier.squash_mask(
2025-03-14T04:33:14.6260708Z             ...     params_to_keep_per_layer={
2025-03-14T04:33:14.6261092Z             ...         'submodule1.linear1': ('foo', 'bar'),
2025-03-14T04:33:14.6261488Z             ...         'submodule2.linear42': ('baz',)
2025-03-14T04:33:14.6261934Z             ...     })
2025-03-14T04:33:14.6262265Z             >>> print(model.submodule1.linear1.sparse_params)
2025-03-14T04:33:14.6262657Z             {'foo': 42, 'bar': 24}
2025-03-14T04:33:14.6263025Z             >>> print(model.submodule2.linear42.sparse_params)
2025-03-14T04:33:14.6263403Z             {'baz': 0.1}
2025-03-14T04:33:14.6263587Z 
2025-03-14T04:33:14.6263715Z             >>> # Keep sparse params for all layers
2025-03-14T04:33:14.6264143Z             >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar'))
2025-03-14T04:33:14.6264608Z             >>> print(model.submodule1.linear1.sparse_params)
2025-03-14T04:33:14.6264993Z             {'foo': 42, 'bar': 24}
2025-03-14T04:33:14.6265362Z             >>> print(model.submodule2.linear42.sparse_params)
2025-03-14T04:33:14.6265748Z             {'foo': 42, 'bar': 24}
2025-03-14T04:33:14.6265945Z 
2025-03-14T04:33:14.6266161Z             >>> # Keep some sparse params for all layers, and specific ones for
2025-03-14T04:33:14.6266605Z             >>> # some other layers
2025-03-14T04:33:14.6266938Z             >>> sparsifier.squash_mask(
2025-03-14T04:33:14.6267293Z             ...     params_to_keep=('foo', 'bar'),
2025-03-14T04:33:14.6267700Z             ...     params_to_keep_per_layer={
2025-03-14T04:33:14.6268070Z             ...         'submodule2.linear42': ('baz',)
2025-03-14T04:33:14.6268414Z             ...     })
2025-03-14T04:33:14.6268733Z             >>> print(model.submodule1.linear1.sparse_params)
2025-03-14T04:33:14.6269122Z             {'foo': 42, 'bar': 24}
2025-03-14T04:33:14.6269498Z             >>> print(model.submodule2.linear42.sparse_params)
2025-03-14T04:33:14.6269902Z             {'foo': 42, 'bar': 24, 'baz': 0.1}
2025-03-14T04:33:14.6270226Z         
2025-03-14T04:33:14.6270615Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.6271000Z 
2025-03-14T04:33:14.7004547Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181.
2025-03-14T04:33:14.7005600Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.7006000Z 
2025-03-14T04:33:14.7006255Z Config object that specifies the supported data types passed as arguments to
2025-03-14T04:33:14.7006873Z quantize ops in the reference model spec, for input and output activations,
2025-03-14T04:33:14.7007342Z weights, and biases.
2025-03-14T04:33:14.7007521Z 
2025-03-14T04:33:14.7007799Z For example, consider the following reference model:
2025-03-14T04:33:14.7008091Z 
2025-03-14T04:33:14.7008258Z   quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2025-03-14T04:33:14.7008542Z 
2025-03-14T04:33:14.7008761Z The pattern in the square brackets refers to the reference pattern of
2025-03-14T04:33:14.7009345Z statically quantized linear. Setting the input dtype as `torch.quint8`
2025-03-14T04:33:14.7009940Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2025-03-14T04:33:14.7010527Z to the first quantize op (quant1). Similarly, setting the output dtype as
2025-03-14T04:33:14.7011107Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2025-03-14T04:33:14.7011572Z the second quantize op (quant2).
2025-03-14T04:33:14.7011789Z 
2025-03-14T04:33:14.7012008Z Note that the dtype here does not refer to the interface dtypes of the
2025-03-14T04:33:14.7012563Z op. For example, the "input dtype" here is not the dtype of the input
2025-03-14T04:33:14.7013128Z tensor passed to the quantized linear op. Though it can still be the
2025-03-14T04:33:14.7013668Z same as the interface dtype, this is not always the case, e.g. the
2025-03-14T04:33:14.7014212Z interface dtype is fp32 in dynamic quantization but the "input dtype"
2025-03-14T04:33:14.7014769Z specified in the DTypeConfig would still be quint8. The semantics of
2025-03-14T04:33:14.7015313Z dtypes here are the same as the semantics of the dtypes specified in
2025-03-14T04:33:14.7015795Z the observers.
2025-03-14T04:33:14.7016000Z 
2025-03-14T04:33:14.7016226Z These dtypes are matched against the ones specified in the user's
2025-03-14T04:33:14.7016777Z QConfig. If there is a match, and the QConfig satisfies the constraints
2025-03-14T04:33:14.7017343Z specified in the DTypeConfig (if any), then we will quantize the given
2025-03-14T04:33:14.7017913Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2025-03-14T04:33:14.7018380Z the pattern will not be quantized.
2025-03-14T04:33:14.7018592Z 
2025-03-14T04:33:14.7018731Z Example usage::
2025-03-14T04:33:14.7018878Z 
2025-03-14T04:33:14.7019004Z     >>> # xdoctest: +SKIP(failing)
2025-03-14T04:33:14.7019330Z     >>> dtype_config1 = DTypeConfig(
2025-03-14T04:33:14.7019667Z     ...     input_dtype=torch.quint8,
2025-03-14T04:33:14.7020011Z     ...     output_dtype=torch.quint8,
2025-03-14T04:33:14.7020337Z     ...     weight_dtype=torch.qint8,
2025-03-14T04:33:14.7020673Z     ...     bias_dtype=torch.float)
2025-03-14T04:33:14.7020896Z 
2025-03-14T04:33:14.7021014Z     >>> dtype_config2 = DTypeConfig(
2025-03-14T04:33:14.7021367Z     ...     input_dtype=DTypeWithConstraints(
2025-03-14T04:33:14.7021770Z     ...         dtype=torch.quint8,
2025-03-14T04:33:14.7022098Z     ...         quant_min_lower_bound=0,
2025-03-14T04:33:14.7022442Z     ...         quant_max_upper_bound=255,
2025-03-14T04:33:14.7022766Z     ...     ),
2025-03-14T04:33:14.7023056Z     ...     output_dtype=DTypeWithConstraints(
2025-03-14T04:33:14.7023426Z     ...         dtype=torch.quint8,
2025-03-14T04:33:14.7023753Z     ...         quant_min_lower_bound=0,
2025-03-14T04:33:14.7024098Z     ...         quant_max_upper_bound=255,
2025-03-14T04:33:14.7024421Z     ...     ),
2025-03-14T04:33:14.7024712Z     ...     weight_dtype=DTypeWithConstraints(
2025-03-14T04:33:14.7025074Z     ...         dtype=torch.qint8,
2025-03-14T04:33:14.7025409Z     ...         quant_min_lower_bound=-128,
2025-03-14T04:33:14.7025766Z     ...         quant_max_upper_bound=127,
2025-03-14T04:33:14.7026085Z     ...     ),
2025-03-14T04:33:14.7026349Z     ...     bias_dtype=torch.float)
2025-03-14T04:33:14.7026574Z 
2025-03-14T04:33:14.7026696Z     >>> dtype_config1.input_dtype
2025-03-14T04:33:14.7027013Z     torch.quint8
2025-03-14T04:33:14.7027182Z 
2025-03-14T04:33:14.7027298Z     >>> dtype_config2.input_dtype
2025-03-14T04:33:14.7027616Z     torch.quint8
2025-03-14T04:33:14.7027779Z 
2025-03-14T04:33:14.7027923Z     >>> dtype_config2.input_dtype_with_constraints
2025-03-14T04:33:14.7028776Z     DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2025-03-14T04:33:14.7029435Z 
2025-03-14T04:33:14.7029692Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.7030081Z 
2025-03-14T04:33:14.8074733Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301.
2025-03-14T04:33:14.8076099Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.8076491Z 
2025-03-14T04:33:14.8076777Z Takes in optional filter values and generates two tables with desired information.
2025-03-14T04:33:14.8077168Z 
2025-03-14T04:33:14.8077399Z The generated tables are presented in both a list-of-lists format
2025-03-14T04:33:14.8077767Z 
2025-03-14T04:33:14.8077985Z The reason for the two tables are that they handle different things:
2025-03-14T04:33:14.8078492Z 1.) the first table handles all tensor level information
2025-03-14T04:33:14.8079005Z 2.) the second table handles and displays all channel based information
2025-03-14T04:33:14.8079495Z 
2025-03-14T04:33:14.8079824Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-14T04:33:14.8080602Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-14T04:33:14.8081626Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-14T04:33:14.8082304Z 
2025-03-14T04:33:14.8082415Z Tensor table columns:
2025-03-14T04:33:14.8082798Z     idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:14.8083287Z     ----  ---------  ---------   ---------   ---------        ---------
2025-03-14T04:33:14.8083565Z 
2025-03-14T04:33:14.8083704Z Per-Channel table columns:
2025-03-14T04:33:14.8084129Z     idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:14.8084652Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-14T04:33:14.8084942Z 
2025-03-14T04:33:14.8085048Z Args:
2025-03-14T04:33:14.8085453Z     feature_filter (str, optional): Filters the features presented to only those that
2025-03-14T04:33:14.8085964Z         contain this filter substring
2025-03-14T04:33:14.8086362Z         Default = "", results in all the features being printed
2025-03-14T04:33:14.8086920Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:14.8087662Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:14.8088028Z 
2025-03-14T04:33:14.8088164Z Returns a dictionary with two keys:
2025-03-14T04:33:14.8088573Z     (Dict[str, Tuple[List, List]]) A dict containing two keys:
2025-03-14T04:33:14.8089009Z     "tensor_level_info", "channel_level_info"
2025-03-14T04:33:14.8089359Z         Each key maps to a tuple with:
2025-03-14T04:33:14.8089711Z             A list of the headers of each table
2025-03-14T04:33:14.8090139Z             A list of lists containing the table information row by row
2025-03-14T04:33:14.8090623Z             The 0th index row will contain the headers of the columns
2025-03-14T04:33:14.8091049Z             The rest of the rows will contain data
2025-03-14T04:33:14.8091304Z 
2025-03-14T04:33:14.8091405Z Example Use:
2025-03-14T04:33:14.8091684Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:14.8092089Z     >>> mod_report_visualizer.generate_filtered_tables(
2025-03-14T04:33:14.8092491Z     ...     feature_filter = "per_channel_min",
2025-03-14T04:33:14.8092850Z     ...     module_fqn_filter = "block1"
2025-03-14T04:33:14.8093348Z     ... ) # generates table with per_channel_min info for all modules in block 1 of the model
2025-03-14T04:33:14.8093749Z 
2025-03-14T04:33:14.8094122Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.8094506Z 
2025-03-14T04:33:14.8095402Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400.
2025-03-14T04:33:14.8096681Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.8097073Z 
2025-03-14T04:33:14.8097351Z Takes in optional filter values and prints out formatted tables of the information.
2025-03-14T04:33:14.8097759Z 
2025-03-14T04:33:14.8098103Z The reason for the two tables printed out instead of one large one are that they handle different things:
2025-03-14T04:33:14.8098739Z 1.) the first table handles all tensor level information
2025-03-14T04:33:14.8099244Z 2.) the second table handles and displays all channel based information
2025-03-14T04:33:14.8099587Z 
2025-03-14T04:33:14.8099907Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-14T04:33:14.8100681Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-14T04:33:14.8101490Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-14T04:33:14.8101981Z 
2025-03-14T04:33:14.8102087Z Tensor table columns:
2025-03-14T04:33:14.8102500Z  idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:14.8103006Z ----  ---------  ---------   ---------   ---------        ---------
2025-03-14T04:33:14.8103294Z 
2025-03-14T04:33:14.8103407Z Per-Channel table columns:
2025-03-14T04:33:14.8103593Z 
2025-03-14T04:33:14.8103832Z  idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:14.8104350Z ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-14T04:33:14.8104634Z 
2025-03-14T04:33:14.8104743Z Args:
2025-03-14T04:33:14.8105148Z     feature_filter (str, optional): Filters the features presented to only those that
2025-03-14T04:33:14.8105659Z         contain this filter substring
2025-03-14T04:33:14.8106053Z         Default = "", results in all the features being printed
2025-03-14T04:33:14.8106605Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:14.8107246Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:14.8107614Z 
2025-03-14T04:33:14.8107729Z Example Use:
2025-03-14T04:33:14.8108012Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:14.8108466Z     >>> mod_report_visualizer.generate_table_visualization(
2025-03-14T04:33:14.8108888Z     ...     feature_filter = "per_channel_min",
2025-03-14T04:33:14.8109253Z     ...     module_fqn_filter = "block1"
2025-03-14T04:33:14.8109559Z     ... )
2025-03-14T04:33:14.8109893Z     >>> # prints out neatly formatted table with per_channel_min info
2025-03-14T04:33:14.8110342Z     >>> # for all modules in block 1 of the model
2025-03-14T04:33:14.8110593Z 
2025-03-14T04:33:14.8110852Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.8111237Z 
2025-03-14T04:33:14.8112175Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566.
2025-03-14T04:33:14.8113447Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.8113826Z 
2025-03-14T04:33:14.8114085Z Takes in a feature and optional module_filter and plots of the desired data.
2025-03-14T04:33:14.8114436Z 
2025-03-14T04:33:14.8114730Z For per channel features, it averages the value across the channels and plots a point
2025-03-14T04:33:14.8115438Z per module. The reason for this is that for models with hundreds of channels, it can
2025-03-14T04:33:14.8116107Z be hard to differentiate one channel line from another, and so the point of generating
2025-03-14T04:33:14.8116785Z a single average point per module is to give a sense of general trends that encourage
2025-03-14T04:33:14.8117284Z further deep dives.
2025-03-14T04:33:14.8117442Z 
2025-03-14T04:33:14.8117547Z Note:
2025-03-14T04:33:14.8117946Z     Only features in the report that have tensor value data are plottable by this class
2025-03-14T04:33:14.8118496Z     When the tensor information is plotted, it will plot:
2025-03-14T04:33:14.8118928Z         idx as the x val, feature value as the y_val
2025-03-14T04:33:14.8119452Z     When the channel information is plotted, it will plot:
2025-03-14T04:33:14.8120017Z         the first idx of each module as the x val, feature value as the y_val [for each channel]
2025-03-14T04:33:14.8120639Z         The reason for this is that we want to be able to compare values across the
2025-03-14T04:33:14.8121240Z         channels for same layer, and it will be hard if values are staggered by idx
2025-03-14T04:33:14.8121785Z         This means each module is represented by only 1 x value
2025-03-14T04:33:14.8122165Z Args:
2025-03-14T04:33:14.8122531Z     feature_filter (str): Filters the features presented to only those that
2025-03-14T04:33:14.8122999Z         contain this filter substring
2025-03-14T04:33:14.8123488Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:14.8124163Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:14.8124574Z 
2025-03-14T04:33:14.8124673Z Example Use:
2025-03-14T04:33:14.8124954Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:14.8125380Z     >>> mod_report_visualizer.generate_plot_visualization(
2025-03-14T04:33:14.8125799Z     ...     feature_filter = "per_channel_min",
2025-03-14T04:33:14.8126161Z     ...     module_fqn_filter = "block1"
2025-03-14T04:33:14.8126480Z     ... )
2025-03-14T04:33:14.8126804Z     >>> # outputs line plot of per_channel_min information for all
2025-03-14T04:33:14.8127301Z     >>> # modules in block1 of model each channel gets it's own line,
2025-03-14T04:33:14.8127795Z     >>> # and it's plotted across the in-order modules on the x-axis
2025-03-14T04:33:14.8128085Z 
2025-03-14T04:33:14.8128354Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.8128722Z 
2025-03-14T04:33:14.8129653Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646.
2025-03-14T04:33:14.8130992Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:14.8131368Z 
2025-03-14T04:33:14.8131662Z Takes in a feature and optional module_filter and plots the histogram of desired data.
2025-03-14T04:33:14.8132058Z 
2025-03-14T04:33:14.8132163Z Note:
2025-03-14T04:33:14.8132566Z     Only features in the report that have tensor value data can be viewed as a histogram
2025-03-14T04:33:14.8133225Z     If you want to plot a histogram from all the channel values of a specific feature for
2025-03-14T04:33:14.8133867Z         a specific model, make sure to specify both the model and the feature properly
2025-03-14T04:33:14.8134492Z         in the filters and you should be able to see a distribution of the channel data
2025-03-14T04:33:14.8134853Z 
2025-03-14T04:33:14.8134960Z Args:
2025-03-14T04:33:14.8135360Z     feature_filter (str, optional): Filters the features presented to only those that
2025-03-14T04:33:14.8135867Z         contain this filter substring
2025-03-14T04:33:14.8136245Z         Default = "", results in all the features being printed
2025-03-14T04:33:14.8136794Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:14.8137463Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:14.8138066Z     num_bins (int, optional): The number of bins to create the histogram with
2025-03-14T04:33:14.8138604Z         Default = 10, the values will be split into 10 equal sized bins
2025-03-14T04:33:14.8138921Z 
2025-03-14T04:33:14.8139018Z Example Use:
2025-03-14T04:33:14.8139273Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:14.8139765Z     >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2025-03-14T04:33:14.8140327Z     ...     feature_filter = "per_channel_min",
2025-03-14T04:33:14.8140692Z     ...     module_fqn_filter = "block1"
2025-03-14T04:33:14.8141008Z     ... )
2025-03-14T04:33:14.8141421Z     # outputs histogram of per_channel_min information for all modules in block1 of model
2025-03-14T04:33:14.8142080Z         information is gathered across all channels for all modules in block 1 for the
2025-03-14T04:33:14.8142687Z         per_channel_min and is displayed in a histogram of equally sized bins
2025-03-14T04:33:14.8143026Z 
2025-03-14T04:33:14.8143298Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:14.8143662Z 
2025-03-14T04:33:15.0484935Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=666.
2025-03-14T04:33:15.0485938Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:15.0486325Z 
2025-03-14T04:33:15.0486921Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2025-03-14T04:33:15.0487674Z The submesh created consists of the dimensions and the communicators indicated by
2025-03-14T04:33:15.0488175Z ``mesh_dim_names``
2025-03-14T04:33:15.0488343Z 
2025-03-14T04:33:15.0488436Z Args:
2025-03-14T04:33:15.0488814Z     mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2025-03-14T04:33:15.0489378Z     mesh dimension of the DeviceMesh to create the submesh for.
2025-03-14T04:33:15.0489804Z Returns:
2025-03-14T04:33:15.0490057Z     A :class:`DeviceMesh` object
2025-03-14T04:33:15.0490256Z 
2025-03-14T04:33:15.0490556Z The following program runs on each process/rank in an SPMD manner in a world size of 8.
2025-03-14T04:33:15.0491072Z In the first example:
2025-03-14T04:33:15.0491504Z     Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2025-03-14T04:33:15.0492130Z     Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2025-03-14T04:33:15.0492740Z     Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2025-03-14T04:33:15.0493320Z     Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2025-03-14T04:33:15.0493961Z     Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2025-03-14T04:33:15.0494563Z     Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2025-03-14T04:33:15.0494907Z 
2025-03-14T04:33:15.0495034Z In the second example:
2025-03-14T04:33:15.0495496Z     Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2025-03-14T04:33:15.0496186Z     Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2025-03-14T04:33:15.0496850Z     Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2025-03-14T04:33:15.0497516Z     Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2025-03-14T04:33:15.0497900Z 
2025-03-14T04:33:15.0498038Z Example::
2025-03-14T04:33:15.0498299Z     >>> # xdoctest: +SKIP("no rank")
2025-03-14T04:33:15.0498693Z     >>> from torch.distributed.device_mesh import DeviceMesh
2025-03-14T04:33:15.0499091Z     >>>
2025-03-14T04:33:15.0499437Z     >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2025-03-14T04:33:15.0499980Z     >>> # of cross-host(dim 0), and within-host (dim 1).
2025-03-14T04:33:15.0500508Z     >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-14T04:33:15.0501012Z     >>> tp_mesh = mesh_2d["tp"]
2025-03-14T04:33:15.0501330Z     >>> dp_mesh = mesh_2d["dp"]
2025-03-14T04:33:15.0501626Z     >>>
2025-03-14T04:33:15.0501874Z     >>> # Initialize a 3D mesh.
2025-03-14T04:33:15.0502367Z     >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2025-03-14T04:33:15.0503091Z     >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2025-03-14T04:33:15.0503645Z     >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2025-03-14T04:33:15.0503999Z     >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2025-03-14T04:33:15.0504221Z 
2025-03-14T04:33:15.0504908Z Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2025-03-14T04:33:15.0505685Z 
2025-03-14T04:33:15.0505944Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-14T04:33:15.0506441Z                                                                                  ^
2025-03-14T04:33:15.0803801Z msg = Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2604.
2025-03-14T04:33:15.0804981Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.0805432Z 
2025-03-14T04:33:15.0805693Z Send or Receive a batch of tensors asynchronously and return a list of requests.
2025-03-14T04:33:15.0806078Z 
2025-03-14T04:33:15.0806355Z Process each of the operations in ``p2p_op_list`` and return the corresponding
2025-03-14T04:33:15.0806924Z requests. NCCL, Gloo, and UCC backend are currently supported.
2025-03-14T04:33:15.0807251Z 
2025-03-14T04:33:15.0807341Z Args:
2025-03-14T04:33:15.0807710Z     p2p_op_list: A list of point-to-point operations(type of each operator is
2025-03-14T04:33:15.0808301Z         ``torch.distributed.P2POp``). The order of the isend/irecv in the list
2025-03-14T04:33:15.0808868Z         matters and it needs to match with corresponding isend/irecv on the
2025-03-14T04:33:15.0809309Z         remote end.
2025-03-14T04:33:15.0809460Z 
2025-03-14T04:33:15.0809571Z Returns:
2025-03-14T04:33:15.0809964Z     A list of distributed request objects returned by calling the corresponding
2025-03-14T04:33:15.0810595Z     op in the op_list.
2025-03-14T04:33:15.0810907Z 
2025-03-14T04:33:15.0811098Z Examples:
2025-03-14T04:33:15.0811473Z     >>> # xdoctest: +SKIP("no rank")
2025-03-14T04:33:15.0811968Z     >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
2025-03-14T04:33:15.0812443Z     >>> recv_tensor = torch.randn(2, dtype=torch.float32)
2025-03-14T04:33:15.0812943Z     >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
2025-03-14T04:33:15.0813404Z     >>> recv_op = dist.P2POp(
2025-03-14T04:33:15.0813811Z     ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
2025-03-14T04:33:15.0814208Z     ... )
2025-03-14T04:33:15.0814487Z     >>> reqs = batch_isend_irecv([send_op, recv_op])
2025-03-14T04:33:15.0814850Z     >>> for req in reqs:
2025-03-14T04:33:15.0815131Z     >>>     req.wait()
2025-03-14T04:33:15.0815397Z     >>> recv_tensor
2025-03-14T04:33:15.0815658Z     tensor([2, 3])     # Rank 0
2025-03-14T04:33:15.0815956Z     tensor([0, 1])     # Rank 1
2025-03-14T04:33:15.0816158Z 
2025-03-14T04:33:15.0816420Z .. note:: Note that when this API is used with the NCCL PG backend, users must set
2025-03-14T04:33:15.0817014Z     the current GPU device with `torch.cuda.set_device`, otherwise it will
2025-03-14T04:33:15.0817480Z     lead to unexpected hang issues.
2025-03-14T04:33:15.0817688Z 
2025-03-14T04:33:15.0817919Z     In addition, if this API is the first collective call in the ``group``
2025-03-14T04:33:15.0818533Z     passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
2025-03-14T04:33:15.0819113Z     this API call; otherwise, the behavior is undefined. If this API call is
2025-03-14T04:33:15.0819688Z     not the first collective call in the ``group``, batched P2P operations
2025-03-14T04:33:15.0820239Z     involving only a subset of ranks of the ``group`` are allowed.
2025-03-14T04:33:15.0820549Z 
2025-03-14T04:33:15.0820821Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.0821189Z 
2025-03-14T04:33:15.0821797Z msg = Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2734.
2025-03-14T04:33:15.0822746Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.0823137Z 
2025-03-14T04:33:15.0823404Z Reduces the tensor data across all machines in a way that all get the final result.
2025-03-14T04:33:15.0823804Z 
2025-03-14T04:33:15.0824041Z After the call ``tensor`` is going to be bitwise identical in all processes.
2025-03-14T04:33:15.0824398Z 
2025-03-14T04:33:15.0824515Z Complex tensors are supported.
2025-03-14T04:33:15.0824726Z 
2025-03-14T04:33:15.0824815Z Args:
2025-03-14T04:33:15.0825154Z     tensor (Tensor): Input and output of the collective. The function
2025-03-14T04:33:15.0825592Z         operates in-place.
2025-03-14T04:33:15.0825906Z     op (optional): One of the values from
2025-03-14T04:33:15.0826268Z         ``torch.distributed.ReduceOp``
2025-03-14T04:33:15.0826900Z         enum.  Specifies an operation used for element-wise reductions.
2025-03-14T04:33:15.0827905Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:15.0828425Z         the default process group will be used.
2025-03-14T04:33:15.0828879Z     async_op (bool, optional): Whether this op should be an async op
2025-03-14T04:33:15.0829188Z 
2025-03-14T04:33:15.0829299Z Returns:
2025-03-14T04:33:15.0829584Z     Async work handle, if async_op is set to True.
2025-03-14T04:33:15.0830004Z     None, if not async_op or if not part of the group
2025-03-14T04:33:15.0830264Z 
2025-03-14T04:33:15.0830359Z Examples:
2025-03-14T04:33:15.0830612Z     >>> # xdoctest: +SKIP("no rank")
2025-03-14T04:33:15.0840743Z     >>> # All tensors below are of torch.int64 type.
2025-03-14T04:33:15.0841272Z     >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:15.0841653Z     >>> device = torch.device(f"cuda:{rank}")
2025-03-14T04:33:15.0842151Z     >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-14T04:33:15.0842623Z     >>> tensor
2025-03-14T04:33:15.0842896Z     tensor([1, 2], device='cuda:0') # Rank 0
2025-03-14T04:33:15.0843361Z     tensor([3, 4], device='cuda:1') # Rank 1
2025-03-14T04:33:15.0843741Z     >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-14T04:33:15.0844095Z     >>> tensor
2025-03-14T04:33:15.0844369Z     tensor([4, 6], device='cuda:0') # Rank 0
2025-03-14T04:33:15.0844733Z     tensor([4, 6], device='cuda:1') # Rank 1
2025-03-14T04:33:15.0844961Z 
2025-03-14T04:33:15.0845124Z     >>> # All tensors below are of torch.cfloat type.
2025-03-14T04:33:15.0845514Z     >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:15.0845868Z     >>> tensor = torch.tensor(
2025-03-14T04:33:15.0846213Z     ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-14T04:33:15.0846593Z     ... ) + 2 * rank * (1 + 1j)
2025-03-14T04:33:15.0846888Z     >>> tensor
2025-03-14T04:33:15.0847184Z     tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-14T04:33:15.0847602Z     tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-14T04:33:15.0848000Z     >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-14T04:33:15.0848355Z     >>> tensor
2025-03-14T04:33:15.0848646Z     tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
2025-03-14T04:33:15.0849256Z     tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
2025-03-14T04:33:15.0849524Z 
2025-03-14T04:33:15.0849528Z 
2025-03-14T04:33:15.0849880Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.0850269Z 
2025-03-14T04:33:15.0850894Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3090.
2025-03-14T04:33:15.0851865Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.0852259Z 
2025-03-14T04:33:15.0852489Z Gathers picklable objects from the whole group in a single process.
2025-03-14T04:33:15.0852846Z 
2025-03-14T04:33:15.0853085Z Similar to :func:`gather`, but Python objects can be passed in. Note that the
2025-03-14T04:33:15.0853599Z object must be picklable in order to be gathered.
2025-03-14T04:33:15.0853877Z 
2025-03-14T04:33:15.0853967Z Args:
2025-03-14T04:33:15.0854239Z     obj (Any): Input object. Must be picklable.
2025-03-14T04:33:15.0854704Z     object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2025-03-14T04:33:15.0855233Z         should be correctly sized as the size of the group for this
2025-03-14T04:33:15.0855771Z         collective and will contain the output. Must be ``None`` on non-dst
2025-03-14T04:33:15.0856224Z         ranks. (default is ``None``)
2025-03-14T04:33:15.0856767Z     dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
2025-03-14T04:33:15.0857410Z         (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
2025-03-14T04:33:15.0858022Z     group: (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:15.0858624Z         the default process group will be used. Default is ``None``.
2025-03-14T04:33:15.0859285Z     group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
2025-03-14T04:33:15.0859747Z 
2025-03-14T04:33:15.0859856Z Returns:
2025-03-14T04:33:15.0860184Z     None. On the ``dst`` rank, ``object_gather_list`` will contain the
2025-03-14T04:33:15.0860619Z     output of the collective.
2025-03-14T04:33:15.0860817Z 
2025-03-14T04:33:15.0861062Z .. note:: Note that this API differs slightly from the gather collective
2025-03-14T04:33:15.0861626Z     since it does not provide an async_op handle and thus will be a blocking
2025-03-14T04:33:15.0862069Z     call.
2025-03-14T04:33:15.0862196Z 
2025-03-14T04:33:15.0862451Z .. note:: For NCCL-based processed groups, internal tensor representations
2025-03-14T04:33:15.0863028Z     of objects must be moved to the GPU device before communication takes
2025-03-14T04:33:15.0863524Z     place. In this case, the device used is given by
2025-03-14T04:33:15.0864018Z     ``torch.cuda.current_device()`` and it is the user's responsiblity to
2025-03-14T04:33:15.0864600Z     ensure that this is set so that each rank has an individual GPU, via
2025-03-14T04:33:15.0865053Z     ``torch.cuda.set_device()``.
2025-03-14T04:33:15.0865270Z 
2025-03-14T04:33:15.0865370Z .. warning::
2025-03-14T04:33:15.0865721Z     :func:`gather_object` uses ``pickle`` module implicitly, which is
2025-03-14T04:33:15.0866362Z     known to be insecure. It is possible to construct malicious pickle data
2025-03-14T04:33:15.0867396Z     which will execute arbitrary code during unpickling. Only call this
2025-03-14T04:33:15.0867895Z     function with data you trust.
2025-03-14T04:33:15.0868121Z 
2025-03-14T04:33:15.0868221Z .. warning::
2025-03-14T04:33:15.0868584Z     Calling :func:`gather_object` with GPU tensors is not well supported
2025-03-14T04:33:15.0869168Z     and inefficient as it incurs GPU -> CPU transfer since tensors would be
2025-03-14T04:33:15.0869706Z     pickled. Please consider using :func:`gather` instead.
2025-03-14T04:33:15.0870003Z 
2025-03-14T04:33:15.0870104Z Example::
2025-03-14T04:33:15.0870391Z     >>> # xdoctest: +SKIP("need process group init")
2025-03-14T04:33:15.0870843Z     >>> # Note: Process group initialization omitted on each rank.
2025-03-14T04:33:15.0871272Z     >>> import torch.distributed as dist
2025-03-14T04:33:15.0871626Z     >>> # Assumes world_size of 3.
2025-03-14T04:33:15.0872073Z     >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2025-03-14T04:33:15.0872505Z     >>> output = [None for _ in gather_objects]
2025-03-14T04:33:15.0872862Z     >>> dist.gather_object(
2025-03-14T04:33:15.0873184Z     ...     gather_objects[dist.get_rank()],
2025-03-14T04:33:15.0873568Z     ...     output if dist.get_rank() == 0 else None,
2025-03-14T04:33:15.0873915Z     ...     dst=0
2025-03-14T04:33:15.0874147Z     ... )
2025-03-14T04:33:15.0874379Z     >>> # On rank 0
2025-03-14T04:33:15.0874635Z     >>> output
2025-03-14T04:33:15.0874882Z     ['foo', 12, {1: 2}]
2025-03-14T04:33:15.0875057Z 
2025-03-14T04:33:15.0875315Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.0875701Z 
2025-03-14T04:33:15.0876292Z msg = Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3666.
2025-03-14T04:33:15.0877231Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.0877622Z 
2025-03-14T04:33:15.0877770Z Gathers tensors from the whole group in a list.
2025-03-14T04:33:15.0878038Z 
2025-03-14T04:33:15.0878186Z Complex and uneven sized tensors are supported.
2025-03-14T04:33:15.0878453Z 
2025-03-14T04:33:15.0878543Z Args:
2025-03-14T04:33:15.0878863Z     tensor_list (list[Tensor]): Output list. It should contain
2025-03-14T04:33:15.0879469Z         correctly-sized tensors to be used for output of the collective.
2025-03-14T04:33:15.0880014Z         Uneven sized tensors are supported.
2025-03-14T04:33:15.0880458Z     tensor (Tensor): Tensor to be broadcast from current process.
2025-03-14T04:33:15.0881019Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:15.0881514Z         the default process group will be used.
2025-03-14T04:33:15.0881967Z     async_op (bool, optional): Whether this op should be an async op
2025-03-14T04:33:15.0882296Z 
2025-03-14T04:33:15.0882391Z Returns:
2025-03-14T04:33:15.0882667Z     Async work handle, if async_op is set to True.
2025-03-14T04:33:15.0883080Z     None, if not async_op or if not part of the group
2025-03-14T04:33:15.0883340Z 
2025-03-14T04:33:15.0883451Z Examples:
2025-03-14T04:33:15.0883736Z     >>> # xdoctest: +SKIP("need process group init")
2025-03-14T04:33:15.0884142Z     >>> # All tensors below are of torch.int64 dtype.
2025-03-14T04:33:15.0884532Z     >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:15.0884904Z     >>> device = torch.device(f"cuda:{rank}")
2025-03-14T04:33:15.0885252Z     >>> tensor_list = [
2025-03-14T04:33:15.0885645Z     ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
2025-03-14T04:33:15.0886110Z     ... ]
2025-03-14T04:33:15.0886329Z     >>> tensor_list
2025-03-14T04:33:15.0886696Z     [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
2025-03-14T04:33:15.0887224Z     [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
2025-03-14T04:33:15.0887776Z     >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-14T04:33:15.0888231Z     >>> tensor
2025-03-14T04:33:15.0888514Z     tensor([1, 2], device='cuda:0') # Rank 0
2025-03-14T04:33:15.0888873Z     tensor([3, 4], device='cuda:1') # Rank 1
2025-03-14T04:33:15.0889236Z     >>> dist.all_gather(tensor_list, tensor)
2025-03-14T04:33:15.0889570Z     >>> tensor_list
2025-03-14T04:33:15.0889933Z     [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
2025-03-14T04:33:15.0890463Z     [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
2025-03-14T04:33:15.0890779Z 
2025-03-14T04:33:15.0890950Z     >>> # All tensors below are of torch.cfloat dtype.
2025-03-14T04:33:15.0891341Z     >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:15.0891685Z     >>> tensor_list = [
2025-03-14T04:33:15.0892076Z     ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
2025-03-14T04:33:15.0892544Z     ... ]
2025-03-14T04:33:15.0892776Z     >>> tensor_list
2025-03-14T04:33:15.0893185Z     [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
2025-03-14T04:33:15.0893806Z     [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
2025-03-14T04:33:15.0894289Z     >>> tensor = torch.tensor(
2025-03-14T04:33:15.0894647Z     ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-14T04:33:15.0895005Z     ... ) + 2 * rank * (1 + 1j)
2025-03-14T04:33:15.0895300Z     >>> tensor
2025-03-14T04:33:15.0895593Z     tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-14T04:33:15.0896000Z     tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-14T04:33:15.0896388Z     >>> dist.all_gather(tensor_list, tensor)
2025-03-14T04:33:15.0896725Z     >>> tensor_list
2025-03-14T04:33:15.0897134Z     [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
2025-03-14T04:33:15.0897758Z     [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
2025-03-14T04:33:15.0898134Z 
2025-03-14T04:33:15.0898139Z 
2025-03-14T04:33:15.0898397Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.0898782Z 
2025-03-14T04:33:15.0924664Z msg = Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4381.
2025-03-14T04:33:15.0925760Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.0926190Z 
2025-03-14T04:33:15.0926447Z Split input tensor and then scatter the split list to all processes in a group.
2025-03-14T04:33:15.0926841Z 
2025-03-14T04:33:15.0927108Z Later the received tensors are concatenated from all the processes in the group
2025-03-14T04:33:15.0927628Z and returned as a single output tensor.
2025-03-14T04:33:15.0927869Z 
2025-03-14T04:33:15.0927990Z Complex tensors are supported.
2025-03-14T04:33:15.0928208Z 
2025-03-14T04:33:15.0928301Z Args:
2025-03-14T04:33:15.0928606Z     output (Tensor): Gathered concatenated output tensor.
2025-03-14T04:33:15.0929035Z     input (Tensor): Input tensor to scatter.
2025-03-14T04:33:15.0929503Z     output_split_sizes: (list[Int], optional): Output split sizes for dim 0
2025-03-14T04:33:15.0930065Z         if specified None or empty, dim 0 of ``output`` tensor must divide
2025-03-14T04:33:15.0930507Z         equally by ``world_size``.
2025-03-14T04:33:15.0930948Z     input_split_sizes: (list[Int], optional): Input split sizes for dim 0
2025-03-14T04:33:15.0931492Z         if specified None or empty, dim 0 of ``input`` tensor must divide
2025-03-14T04:33:15.0931971Z         equally by ``world_size``.
2025-03-14T04:33:15.0932422Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:15.0932915Z         the default process group will be used.
2025-03-14T04:33:15.0933371Z     async_op (bool, optional): Whether this op should be an async op.
2025-03-14T04:33:15.0933686Z 
2025-03-14T04:33:15.0933795Z Returns:
2025-03-14T04:33:15.0934084Z     Async work handle, if async_op is set to True.
2025-03-14T04:33:15.0934506Z     None, if not async_op or if not part of the group.
2025-03-14T04:33:15.0934784Z 
2025-03-14T04:33:15.0934907Z .. warning::
2025-03-14T04:33:15.0935237Z     `all_to_all_single` is experimental and subject to change.
2025-03-14T04:33:15.0935522Z 
2025-03-14T04:33:15.0935637Z Examples:
2025-03-14T04:33:15.0935911Z     >>> # xdoctest: +SKIP("Undefined rank")
2025-03-14T04:33:15.0936356Z     >>> input = torch.arange(4) + rank * 4
2025-03-14T04:33:15.0936858Z     >>> input
2025-03-14T04:33:15.0937312Z     tensor([0, 1, 2, 3])     # Rank 0
2025-03-14T04:33:15.0937810Z     tensor([4, 5, 6, 7])     # Rank 1
2025-03-14T04:33:15.0938131Z     tensor([8, 9, 10, 11])   # Rank 2
2025-03-14T04:33:15.0938447Z     tensor([12, 13, 14, 15]) # Rank 3
2025-03-14T04:33:15.0938839Z     >>> output = torch.empty([4], dtype=torch.int64)
2025-03-14T04:33:15.0939237Z     >>> dist.all_to_all_single(output, input)
2025-03-14T04:33:15.0939572Z     >>> output
2025-03-14T04:33:15.0939839Z     tensor([0, 4, 8, 12])    # Rank 0
2025-03-14T04:33:15.0940159Z     tensor([1, 5, 9, 13])    # Rank 1
2025-03-14T04:33:15.0940476Z     tensor([2, 6, 10, 14])   # Rank 2
2025-03-14T04:33:15.0940794Z     tensor([3, 7, 11, 15])   # Rank 3
2025-03-14T04:33:15.0941007Z 
2025-03-14T04:33:15.0941177Z     >>> # Essentially, it is similar to following operation:
2025-03-14T04:33:15.0941614Z     >>> scatter_list = list(input.chunk(world_size))
2025-03-14T04:33:15.0942017Z     >>> gather_list = list(output.chunk(world_size))
2025-03-14T04:33:15.0942388Z     >>> for i in range(world_size):
2025-03-14T04:33:15.0942833Z     >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
2025-03-14T04:33:15.0943195Z 
2025-03-14T04:33:15.0943322Z     >>> # Another example with uneven split
2025-03-14T04:33:15.0943651Z     >>> input
2025-03-14T04:33:15.0943957Z     tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-14T04:33:15.0944406Z     tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-14T04:33:15.0944858Z     tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-14T04:33:15.0945307Z     tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-14T04:33:15.0945696Z     >>> input_splits
2025-03-14T04:33:15.0946055Z     [2, 2, 1, 1]                                                     # Rank 0
2025-03-14T04:33:15.0946466Z     [3, 2, 2, 2]                                                     # Rank 1
2025-03-14T04:33:15.0946849Z     [2, 1, 1, 1]                                                     # Rank 2
2025-03-14T04:33:15.0947228Z     [2, 2, 2, 1]                                                     # Rank 3
2025-03-14T04:33:15.0947583Z     >>> output_splits
2025-03-14T04:33:15.0947882Z     [2, 3, 2, 2]                                                     # Rank 0
2025-03-14T04:33:15.0948257Z     [2, 2, 1, 2]                                                     # Rank 1
2025-03-14T04:33:15.0948629Z     [1, 2, 1, 2]                                                     # Rank 2
2025-03-14T04:33:15.0949256Z     [1, 2, 1, 1]                                                     # Rank 3
2025-03-14T04:33:15.0949588Z     >>> output = ...
2025-03-14T04:33:15.0949952Z     >>> dist.all_to_all_single(output, input, output_splits, input_splits)
2025-03-14T04:33:15.0950368Z     >>> output
2025-03-14T04:33:15.0950670Z     tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
2025-03-14T04:33:15.0951107Z     tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
2025-03-14T04:33:15.0951622Z     tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
2025-03-14T04:33:15.0952061Z     tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
2025-03-14T04:33:15.0952341Z 
2025-03-14T04:33:15.0952346Z 
2025-03-14T04:33:15.0952514Z     >>> # Another example with tensors of torch.cfloat type.
2025-03-14T04:33:15.0952901Z     >>> input = torch.tensor(
2025-03-14T04:33:15.0953234Z     ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-14T04:33:15.0953593Z     ... ) + 4 * rank * (1 + 1j)
2025-03-14T04:33:15.0953870Z     >>> input
2025-03-14T04:33:15.0954177Z     tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
2025-03-14T04:33:15.0954649Z     tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
2025-03-14T04:33:15.0955126Z     tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
2025-03-14T04:33:15.0955616Z     tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
2025-03-14T04:33:15.0956065Z     >>> output = torch.empty([4], dtype=torch.int64)
2025-03-14T04:33:15.0956446Z     >>> dist.all_to_all_single(output, input)
2025-03-14T04:33:15.0956770Z     >>> output
2025-03-14T04:33:15.0957124Z     tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
2025-03-14T04:33:15.0957602Z     tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
2025-03-14T04:33:15.0958077Z     tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
2025-03-14T04:33:15.0958552Z     tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
2025-03-14T04:33:15.0958841Z 
2025-03-14T04:33:15.0959169Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.0959551Z 
2025-03-14T04:33:15.0960157Z msg = Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4523.
2025-03-14T04:33:15.0961090Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.0961461Z 
2025-03-14T04:33:15.0961840Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
2025-03-14T04:33:15.0962318Z 
2025-03-14T04:33:15.0962451Z Complex tensors are supported.
2025-03-14T04:33:15.0962648Z 
2025-03-14T04:33:15.0962749Z Args:
2025-03-14T04:33:15.0963094Z     output_tensor_list (list[Tensor]): List of tensors to be gathered one
2025-03-14T04:33:15.0963523Z         per rank.
2025-03-14T04:33:15.0963902Z     input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
2025-03-14T04:33:15.0964485Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:15.0965055Z         the default process group will be used.
2025-03-14T04:33:15.0965500Z     async_op (bool, optional): Whether this op should be an async op.
2025-03-14T04:33:15.0965815Z 
2025-03-14T04:33:15.0965921Z Returns:
2025-03-14T04:33:15.0966185Z     Async work handle, if async_op is set to True.
2025-03-14T04:33:15.0966574Z     None, if not async_op or if not part of the group.
2025-03-14T04:33:15.0966842Z 
2025-03-14T04:33:15.0966944Z .. warning::
2025-03-14T04:33:15.0967235Z     `all_to_all` is experimental and subject to change.
2025-03-14T04:33:15.0967499Z 
2025-03-14T04:33:15.0967592Z Examples:
2025-03-14T04:33:15.0967844Z     >>> # xdoctest: +SKIP("Undefined rank")
2025-03-14T04:33:15.0968193Z     >>> input = torch.arange(4) + rank * 4
2025-03-14T04:33:15.0968535Z     >>> input = list(input.chunk(4))
2025-03-14T04:33:15.0968838Z     >>> input
2025-03-14T04:33:15.0969153Z     [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
2025-03-14T04:33:15.0969610Z     [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
2025-03-14T04:33:15.0970060Z     [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
2025-03-14T04:33:15.0970541Z     [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
2025-03-14T04:33:15.0971015Z     >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-14T04:33:15.0971425Z     >>> dist.all_to_all(output, input)
2025-03-14T04:33:15.0971739Z     >>> output
2025-03-14T04:33:15.0972051Z     [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
2025-03-14T04:33:15.0972503Z     [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
2025-03-14T04:33:15.0972951Z     [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
2025-03-14T04:33:15.0973401Z     [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
2025-03-14T04:33:15.0973683Z 
2025-03-14T04:33:15.0973869Z     >>> # Essentially, it is similar to following operation:
2025-03-14T04:33:15.0974269Z     >>> scatter_list = input
2025-03-14T04:33:15.0974576Z     >>> gather_list = output
2025-03-14T04:33:15.0974867Z     >>> for i in range(world_size):
2025-03-14T04:33:15.0975313Z     >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
2025-03-14T04:33:15.0975674Z 
2025-03-14T04:33:15.0975769Z     >>> input
2025-03-14T04:33:15.0976084Z     tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-14T04:33:15.0976570Z     tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-14T04:33:15.0977028Z     tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-14T04:33:15.0977483Z     tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-14T04:33:15.0977877Z     >>> input_splits
2025-03-14T04:33:15.0978184Z     [2, 2, 1, 1]                                                     # Rank 0
2025-03-14T04:33:15.0978581Z     [3, 2, 2, 2]                                                     # Rank 1
2025-03-14T04:33:15.0978974Z     [2, 1, 1, 1]                                                     # Rank 2
2025-03-14T04:33:15.0979364Z     [2, 2, 2, 1]                                                     # Rank 3
2025-03-14T04:33:15.0979725Z     >>> output_splits
2025-03-14T04:33:15.0980033Z     [2, 3, 2, 2]                                                     # Rank 0
2025-03-14T04:33:15.0980425Z     [2, 2, 1, 2]                                                     # Rank 1
2025-03-14T04:33:15.0980818Z     [1, 2, 1, 2]                                                     # Rank 2
2025-03-14T04:33:15.0981216Z     [1, 2, 1, 1]                                                     # Rank 3
2025-03-14T04:33:15.0981608Z     >>> input = list(input.split(input_splits))
2025-03-14T04:33:15.0981949Z     >>> input
2025-03-14T04:33:15.0982312Z     [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
2025-03-14T04:33:15.0982862Z     [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
2025-03-14T04:33:15.0983738Z     [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
2025-03-14T04:33:15.0984571Z     [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
2025-03-14T04:33:15.0985324Z     >>> output = ...
2025-03-14T04:33:15.0985615Z     >>> dist.all_to_all(output, input)
2025-03-14T04:33:15.0985921Z     >>> output
2025-03-14T04:33:15.0986278Z     [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
2025-03-14T04:33:15.0986814Z     [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
2025-03-14T04:33:15.0987352Z     [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
2025-03-14T04:33:15.0987888Z     [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
2025-03-14T04:33:15.0988227Z 
2025-03-14T04:33:15.0988395Z     >>> # Another example with tensors of torch.cfloat type.
2025-03-14T04:33:15.0988794Z     >>> input = torch.tensor(
2025-03-14T04:33:15.0989140Z     ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-14T04:33:15.0989510Z     ... ) + 4 * rank * (1 + 1j)
2025-03-14T04:33:15.0989862Z     >>> input = list(input.chunk(4))
2025-03-14T04:33:15.0990178Z     >>> input
2025-03-14T04:33:15.0990547Z     [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
2025-03-14T04:33:15.0991108Z     [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
2025-03-14T04:33:15.0991671Z     [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
2025-03-14T04:33:15.0992242Z     [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
2025-03-14T04:33:15.0992784Z     >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-14T04:33:15.0993212Z     >>> dist.all_to_all(output, input)
2025-03-14T04:33:15.0993534Z     >>> output
2025-03-14T04:33:15.0993899Z     [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
2025-03-14T04:33:15.0994462Z     [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
2025-03-14T04:33:15.0995021Z     [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
2025-03-14T04:33:15.0995577Z     [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
2025-03-14T04:33:15.0995908Z 
2025-03-14T04:33:15.0995912Z 
2025-03-14T04:33:15.0996217Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.0996590Z 
2025-03-14T04:33:15.0997130Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2.
2025-03-14T04:33:15.0997993Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.0998385Z 
2025-03-14T04:33:15.0998512Z Module ``torch.distributed.launch``.
2025-03-14T04:33:15.0998751Z 
2025-03-14T04:33:15.0999011Z ``torch.distributed.launch`` is a module that spawns up multiple distributed
2025-03-14T04:33:15.0999616Z training processes on each of the training nodes.
2025-03-14T04:33:15.0999902Z 
2025-03-14T04:33:15.1000004Z .. warning::
2025-03-14T04:33:15.1000156Z 
2025-03-14T04:33:15.1000414Z     This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2025-03-14T04:33:15.1000803Z 
2025-03-14T04:33:15.1001046Z The utility can be used for single-node distributed training, in which one or
2025-03-14T04:33:15.1001651Z more processes per node will be spawned. The utility can be used for either
2025-03-14T04:33:15.1002233Z CPU training or GPU training. If the utility is used for GPU training,
2025-03-14T04:33:15.1002821Z each distributed process will be operating on a single GPU. This can achieve
2025-03-14T04:33:15.1003436Z well-improved single-node training performance. It can also be used in
2025-03-14T04:33:15.1004099Z multi-node distributed training, by spawning up multiple processes on each node
2025-03-14T04:33:15.1004757Z for well-improved multi-node distributed training performance as well.
2025-03-14T04:33:15.1005347Z This will especially be beneficial for systems with multiple Infiniband
2025-03-14T04:33:15.1005961Z interfaces that have direct-GPU support, since all of them can be utilized for
2025-03-14T04:33:15.1006459Z aggregated communication bandwidth.
2025-03-14T04:33:15.1006677Z 
2025-03-14T04:33:15.1006931Z In both cases of single-node distributed training or multi-node distributed
2025-03-14T04:33:15.1007531Z training, this utility will launch the given number of processes per node
2025-03-14T04:33:15.1008120Z (``--nproc-per-node``). If used for GPU training, this number needs to be less
2025-03-14T04:33:15.1008693Z or equal to the number of GPUs on the current system (``nproc_per_node``),
2025-03-14T04:33:15.1009245Z and each process will be operating on a single GPU from *GPU 0 to
2025-03-14T04:33:15.1009685Z GPU (nproc_per_node - 1)*.
2025-03-14T04:33:15.1009871Z 
2025-03-14T04:33:15.1009995Z **How to use this module:**
2025-03-14T04:33:15.1010178Z 
2025-03-14T04:33:15.1010348Z 1. Single-Node multi-process distributed training
2025-03-14T04:33:15.1010647Z 
2025-03-14T04:33:15.1010755Z ::
2025-03-14T04:33:15.1010872Z 
2025-03-14T04:33:15.1011134Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-14T04:33:15.1011696Z                YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2025-03-14T04:33:15.1012152Z                arguments of your training script)
2025-03-14T04:33:15.1012391Z 
2025-03-14T04:33:15.1012624Z 2. Multi-Node multi-process distributed training: (e.g. two nodes)
2025-03-14T04:33:15.1012947Z 
2025-03-14T04:33:15.1012951Z 
2025-03-14T04:33:15.1013118Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2025-03-14T04:33:15.1013374Z 
2025-03-14T04:33:15.1013483Z ::
2025-03-14T04:33:15.1013601Z 
2025-03-14T04:33:15.1013854Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-14T04:33:15.1014386Z                --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2025-03-14T04:33:15.1014889Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-14T04:33:15.1015373Z                and all other arguments of your training script)
2025-03-14T04:33:15.1015657Z 
2025-03-14T04:33:15.1015749Z Node 2:
2025-03-14T04:33:15.1015887Z 
2025-03-14T04:33:15.1015978Z ::
2025-03-14T04:33:15.1016111Z 
2025-03-14T04:33:15.1016411Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-14T04:33:15.1016942Z                --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2025-03-14T04:33:15.1017443Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-14T04:33:15.1017946Z                and all other arguments of your training script)
2025-03-14T04:33:15.1018231Z 
2025-03-14T04:33:15.1018403Z 3. To look up what optional arguments this module offers:
2025-03-14T04:33:15.1018697Z 
2025-03-14T04:33:15.1018791Z ::
2025-03-14T04:33:15.1018927Z 
2025-03-14T04:33:15.1019075Z     python -m torch.distributed.launch --help
2025-03-14T04:33:15.1019343Z 
2025-03-14T04:33:15.1019351Z 
2025-03-14T04:33:15.1019459Z **Important Notices:**
2025-03-14T04:33:15.1019645Z 
2025-03-14T04:33:15.1019843Z 1. This utility and multi-process distributed (single-node or
2025-03-14T04:33:15.1020415Z multi-node) GPU training currently only achieves the best performance using
2025-03-14T04:33:15.1021053Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2025-03-14T04:33:15.1021541Z use for GPU training.
2025-03-14T04:33:15.1021722Z 
2025-03-14T04:33:15.1021947Z 2. In your training program, you must parse the command-line argument:
2025-03-14T04:33:15.1022534Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2025-03-14T04:33:15.1023127Z If your training program uses GPUs, you should ensure that your code only
2025-03-14T04:33:15.1023716Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2025-03-14T04:33:15.1024109Z 
2025-03-14T04:33:15.1024224Z Parsing the local_rank argument
2025-03-14T04:33:15.1024440Z 
2025-03-14T04:33:15.1024535Z ::
2025-03-14T04:33:15.1024654Z 
2025-03-14T04:33:15.1024776Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.1025068Z     >>> import argparse
2025-03-14T04:33:15.1025377Z     >>> parser = argparse.ArgumentParser()
2025-03-14T04:33:15.1025822Z     >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2025-03-14T04:33:15.1026256Z     >>> args = parser.parse_args()
2025-03-14T04:33:15.1026461Z 
2025-03-14T04:33:15.1026604Z Set your device to local rank using either
2025-03-14T04:33:15.1026836Z 
2025-03-14T04:33:15.1026938Z ::
2025-03-14T04:33:15.1027053Z 
2025-03-14T04:33:15.1027271Z     >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2025-03-14T04:33:15.1027588Z 
2025-03-14T04:33:15.1027690Z or
2025-03-14T04:33:15.1027803Z 
2025-03-14T04:33:15.1027907Z ::
2025-03-14T04:33:15.1028018Z 
2025-03-14T04:33:15.1028174Z     >>> with torch.cuda.device(args.local_rank):
2025-03-14T04:33:15.1028532Z     >>>    # your code to run
2025-03-14T04:33:15.1028802Z     >>>    ...
2025-03-14T04:33:15.1028984Z 
2025-03-14T04:33:15.1029096Z .. versionchanged:: 2.0.0
2025-03-14T04:33:15.1029286Z 
2025-03-14T04:33:15.1029536Z     The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2025-03-14T04:33:15.1030154Z     From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2025-03-14T04:33:15.1030674Z     previously used underscored ``--local_rank``.
2025-03-14T04:33:15.1030942Z 
2025-03-14T04:33:15.1031186Z     For backward compatibility, it may be necessary for users to handle both
2025-03-14T04:33:15.1031824Z     cases in their argument parsing code. This means including both ``"--local-rank"``
2025-03-14T04:33:15.1032437Z     and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2025-03-14T04:33:15.1033036Z     provided, the launcher will trigger an error: "error: unrecognized arguments:
2025-03-14T04:33:15.1033657Z     --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2025-03-14T04:33:15.1034171Z     including ``"--local-rank"`` should be sufficient.
2025-03-14T04:33:15.1034452Z 
2025-03-14T04:33:15.1034690Z 3. In your training program, you are supposed to call the following function
2025-03-14T04:33:15.1035301Z at the beginning to start the distributed backend. It is strongly recommended
2025-03-14T04:33:15.1035942Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2025-03-14T04:33:15.1036485Z but ``env://`` is the one that is officially supported by this module.
2025-03-14T04:33:15.1036812Z 
2025-03-14T04:33:15.1036905Z ::
2025-03-14T04:33:15.1037038Z 
2025-03-14T04:33:15.1037251Z     >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2025-03-14T04:33:15.1037729Z     >>>                                      init_method='env://')
2025-03-14T04:33:15.1037987Z 
2025-03-14T04:33:15.1038232Z 4. In your training program, you can either use regular distributed functions
2025-03-14T04:33:15.1038841Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2025-03-14T04:33:15.1039496Z training program uses GPUs for training and you would like to use
2025-03-14T04:33:15.1040035Z :func:`torch.nn.parallel.DistributedDataParallel` module,
2025-03-14T04:33:15.1040462Z here is how to configure it.
2025-03-14T04:33:15.1040651Z 
2025-03-14T04:33:15.1040757Z ::
2025-03-14T04:33:15.1040873Z 
2025-03-14T04:33:15.1041091Z     >>> model = torch.nn.parallel.DistributedDataParallel(model,
2025-03-14T04:33:15.1041553Z     >>>                                                   device_ids=[args.local_rank],
2025-03-14T04:33:15.1041973Z     >>>                                                   output_device=args.local_rank)
2025-03-14T04:33:15.1042235Z 
2025-03-14T04:33:15.1042498Z Please ensure that ``device_ids`` argument is set to be the only GPU device id
2025-03-14T04:33:15.1043139Z that your code will be operating on. This is generally the local rank of the
2025-03-14T04:33:15.1043764Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2025-03-14T04:33:15.1044351Z and ``output_device`` needs to be ``args.local_rank`` in order to use this
2025-03-14T04:33:15.1044782Z utility
2025-03-14T04:33:15.1044905Z 
2025-03-14T04:33:15.1045165Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2025-03-14T04:33:15.1045759Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2025-03-14T04:33:15.1046326Z ``--use-env=True``. You must adjust the subprocess example above to replace
2025-03-14T04:33:15.1046868Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2025-03-14T04:33:15.1047352Z will not pass ``--local-rank`` when you specify this flag.
2025-03-14T04:33:15.1047649Z 
2025-03-14T04:33:15.1047745Z .. warning::
2025-03-14T04:33:15.1047894Z 
2025-03-14T04:33:15.1048100Z     ``local_rank`` is NOT globally unique: it is only unique per process
2025-03-14T04:33:15.1048623Z     on a machine.  Thus, don't use it to decide if you should, e.g.,
2025-03-14T04:33:15.1049219Z     write to a networked filesystem.  See
2025-03-14T04:33:15.1049747Z     https://github.com/pytorch/pytorch/issues/12042 for an example of
2025-03-14T04:33:15.1050259Z     how things can go wrong if you don't do this correctly.
2025-03-14T04:33:15.1050550Z 
2025-03-14T04:33:15.1050554Z 
2025-03-14T04:33:15.1050557Z 
2025-03-14T04:33:15.1050561Z 
2025-03-14T04:33:15.1050817Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.1051199Z 
2025-03-14T04:33:15.1413885Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361.
2025-03-14T04:33:15.1414955Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.1415350Z 
2025-03-14T04:33:15.1415620Z Creates an :class:`ShardedTensor` from local shards and the global metadata.
2025-03-14T04:33:15.1416168Z Needs to be called on all ranks in an SPMD fashion.
2025-03-14T04:33:15.1416430Z 
2025-03-14T04:33:15.1416543Z Args:
2025-03-14T04:33:15.1416956Z     local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2025-03-14T04:33:15.1417538Z         of shards that represent the local shards on this rank.
2025-03-14T04:33:15.1418166Z     global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2025-03-14T04:33:15.1418654Z         shape of the overall sharded tensor.
2025-03-14T04:33:15.1418901Z 
2025-03-14T04:33:15.1419000Z Keyword args:
2025-03-14T04:33:15.1419424Z     process_group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:15.1419956Z         the default process group will be used.
2025-03-14T04:33:15.1420392Z     init_rrefs (bool, optional): Whether or not to initialize
2025-03-14T04:33:15.1420909Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-14T04:33:15.1421453Z         Need to initialize the RPC Framework if specified as ``True``.
2025-03-14T04:33:15.1421886Z         Default: ``False``.
2025-03-14T04:33:15.1422086Z 
2025-03-14T04:33:15.1422181Z Returns:
2025-03-14T04:33:15.1422480Z     A :class:`ShardedTensor` object handle on this rank
2025-03-14T04:33:15.1422765Z 
2025-03-14T04:33:15.1422769Z 
2025-03-14T04:33:15.1422868Z Examples:
2025-03-14T04:33:15.1423271Z     Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2025-03-14T04:33:15.1423851Z     each shard have a (5, 5) local tensor, we can do it like below:
2025-03-14T04:33:15.1424166Z 
2025-03-14T04:33:15.1424267Z     on rank 0:
2025-03-14T04:33:15.1424553Z     >>> # xdoctest: +SKIP("not distributed")
2025-03-14T04:33:15.1424930Z     >>> local_shard_metadata = ShardMetadata(
2025-03-14T04:33:15.1425291Z     >>>     shard_offsets=[0, 0],
2025-03-14T04:33:15.1425618Z     >>>     shard_lengths=[5, 5],
2025-03-14T04:33:15.1426020Z     >>>     placement="rank:0/cuda:0"
2025-03-14T04:33:15.1426383Z     >>> )
2025-03-14T04:33:15.1426722Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-14T04:33:15.1427249Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-14T04:33:15.1427562Z 
2025-03-14T04:33:15.1427674Z     on rank 1:
2025-03-14T04:33:15.1427937Z     >>> # xdoctest: +SKIP("not distributed")
2025-03-14T04:33:15.1428303Z     >>> local_shard_metadata = ShardMetadata(
2025-03-14T04:33:15.1428661Z     >>>     shard_offsets=[5, 0],
2025-03-14T04:33:15.1428972Z     >>>     shard_lengths=[5, 5],
2025-03-14T04:33:15.1429288Z     >>>     placement="rank:1/cuda:1"
2025-03-14T04:33:15.1429594Z     >>> )
2025-03-14T04:33:15.1429925Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-14T04:33:15.1430444Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-14T04:33:15.1430766Z 
2025-03-14T04:33:15.1431024Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.1431407Z 
2025-03-14T04:33:15.1512421Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=799.
2025-03-14T04:33:15.1513640Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.1514034Z 
2025-03-14T04:33:15.1514301Z Initialize a ShardedTensor given only one local tensor, global sharded tensor
2025-03-14T04:33:15.1514807Z size and sharding spec on each rank.
2025-03-14T04:33:15.1515039Z 
2025-03-14T04:33:15.1515129Z Args:
2025-03-14T04:33:15.1515498Z     local_tensor (Tensor): Single tensor of local shard stored in each rank.
2025-03-14T04:33:15.1516125Z     sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2025-03-14T04:33:15.1516688Z         The specification describing how to shard the Tensor.
2025-03-14T04:33:15.1517159Z     global_size (Sequence[int]): Size of the sharded tensor.
2025-03-14T04:33:15.1517715Z     process_group (ProcessGroup, optional): The process group to aggregate on.
2025-03-14T04:33:15.1518197Z         Default: None
2025-03-14T04:33:15.1518551Z     init_rrefs (bool, optional): Whether or not to initialize
2025-03-14T04:33:15.1519171Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-14T04:33:15.1520119Z         Need to initialize the RPC Framework if specified as ``True``.
2025-03-14T04:33:15.1520622Z         Default: ``False``.
2025-03-14T04:33:15.1520807Z 
2025-03-14T04:33:15.1520916Z Returns:
2025-03-14T04:33:15.1521464Z     A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2025-03-14T04:33:15.1522223Z         tensor stored in the current rank.
2025-03-14T04:33:15.1522452Z 
2025-03-14T04:33:15.1522562Z Examples:
2025-03-14T04:33:15.1522809Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.1523137Z     >>> # All tensors below are of torch.int64 type.
2025-03-14T04:33:15.1523510Z     >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:15.1523929Z     >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2025-03-14T04:33:15.1524440Z     >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2025-03-14T04:33:15.1524862Z     >>> local_tensor
2025-03-14T04:33:15.1525132Z     tensor([[1, 2, 3, 4]]) # Rank 0
2025-03-14T04:33:15.1525445Z     tensor([[3, 4, 5, 6]]) # Rank 1
2025-03-14T04:33:15.1525752Z     >>> sharding_dim = 0
2025-03-14T04:33:15.1526063Z     >>> sharding_spec = ChunkShardingSpec(
2025-03-14T04:33:15.1526414Z             dim=sharding_dim,
2025-03-14T04:33:15.1526712Z             placements=[
2025-03-14T04:33:15.1526995Z                 "rank:0/cuda:0",
2025-03-14T04:33:15.1527295Z                 "rank:1/cuda:1",
2025-03-14T04:33:15.1527583Z             ],
2025-03-14T04:33:15.1527816Z         )
2025-03-14T04:33:15.1528084Z     >>> st = ShardedTensor._init_from_local_tensor(
2025-03-14T04:33:15.1528461Z     ...     local_tensor, sharding_spec, [2, 4]
2025-03-14T04:33:15.1528910Z     ... )
2025-03-14T04:33:15.1529122Z     >>> st
2025-03-14T04:33:15.1529356Z     ShardedTensor(
2025-03-14T04:33:15.1529632Z         ShardedTensorMetadata(
2025-03-14T04:33:15.1529948Z             shards_metadata=[
2025-03-14T04:33:15.1530417Z                 ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2025-03-14T04:33:15.1531073Z                 ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2025-03-14T04:33:15.1531559Z             ],
2025-03-14T04:33:15.1531815Z             size=torch.Size([2, 4])
2025-03-14T04:33:15.1532122Z     )
2025-03-14T04:33:15.1532364Z     >>> st.local_tensor()
2025-03-14T04:33:15.1532654Z     tensor([1, 2, 3, 4]) # Rank 0
2025-03-14T04:33:15.1532962Z     tensor([3, 4, 5, 6]) # Rank 1
2025-03-14T04:33:15.1533153Z 
2025-03-14T04:33:15.1533441Z Warning: This API is experimental and subject to change. It lacks of a fully across
2025-03-14T04:33:15.1534080Z          rank validations, and we only validate the local shard on the current rank.
2025-03-14T04:33:15.1534669Z          We fully rely on the user to ensure local tensor is sharded based on the
2025-03-14T04:33:15.1535172Z          sharding spec.
2025-03-14T04:33:15.1535337Z 
2025-03-14T04:33:15.1535609Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.1535978Z 
2025-03-14T04:33:15.1536699Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1040.
2025-03-14T04:33:15.1537761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.1538155Z 
2025-03-14T04:33:15.1538410Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2025-03-14T04:33:15.1538897Z single local shard.
2025-03-14T04:33:15.1539052Z 
2025-03-14T04:33:15.1539294Z If ``resharding_spec`` is same as the original one, this becomes a no-op.
2025-03-14T04:33:15.1539901Z If only ``resharding_spec`` shares the same sharding dim with the original one,
2025-03-14T04:33:15.1540389Z we swap local shards directly.
2025-03-14T04:33:15.1540864Z For more generic cases, we merge different shards across different ranks and split
2025-03-14T04:33:15.1541509Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2025-03-14T04:33:15.1541879Z 
2025-03-14T04:33:15.1541992Z Args:
2025-03-14T04:33:15.1542453Z     resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2025-03-14T04:33:15.1543051Z         specification describing how the tensor is sharded.
2025-03-14T04:33:15.1543334Z 
2025-03-14T04:33:15.1543446Z Returns:
2025-03-14T04:33:15.1543799Z     A :class:`ShardedTensor` object whose local shards are resharded.
2025-03-14T04:33:15.1544121Z 
2025-03-14T04:33:15.1544234Z Examples:
2025-03-14T04:33:15.1544484Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.1544784Z     >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:15.1545217Z     >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2025-03-14T04:33:15.1545647Z     >>> tensor = torch.stack([tensor, tensor])
2025-03-14T04:33:15.1545997Z     >>> tensor
2025-03-14T04:33:15.1546275Z     tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2025-03-14T04:33:15.1546641Z     tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2025-03-14T04:33:15.1547001Z     tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2025-03-14T04:33:15.1547373Z     tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2025-03-14T04:33:15.1547720Z     >>> sharding_dim = 0
2025-03-14T04:33:15.1548016Z     >>> spec = ChunkShardingSpec(
2025-03-14T04:33:15.1548335Z             dim=sharding_dim,
2025-03-14T04:33:15.1548635Z             placements=[
2025-03-14T04:33:15.1549113Z                 "rank:0/cuda:0",
2025-03-14T04:33:15.1549415Z                 "rank:1/cuda:1",
2025-03-14T04:33:15.1549711Z                 "rank:2/cuda:2",
2025-03-14T04:33:15.1550009Z                 "rank:3/cuda:3",
2025-03-14T04:33:15.1550404Z             ],
2025-03-14T04:33:15.1550628Z         )
2025-03-14T04:33:15.1550878Z     >>> current_offsets = [0] * 2
2025-03-14T04:33:15.1551204Z     >>> current_offsets[0] = rank * 2
2025-03-14T04:33:15.1551554Z     >>> shard_metadata = ShardMetadata(
2025-03-14T04:33:15.1551944Z             shard_offsets=copy.deepcopy(current_offsets),
2025-03-14T04:33:15.1552332Z             shard_sizes=tensor.size(),
2025-03-14T04:33:15.1552687Z             placement=spec.placements[rank],
2025-03-14T04:33:15.1553024Z         )
2025-03-14T04:33:15.1553262Z     >>> local_shards = [
2025-03-14T04:33:15.1553536Z             Shard(
2025-03-14T04:33:15.1553795Z                 tensor=tensor,
2025-03-14T04:33:15.1554110Z                 metadata=shard_metadata,
2025-03-14T04:33:15.1554432Z             )
2025-03-14T04:33:15.1554665Z         ]
2025-03-14T04:33:15.1555034Z     >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2025-03-14T04:33:15.1555482Z     >>> sharding_dim = 1
2025-03-14T04:33:15.1555799Z     >>> resharding_spec = ChunkShardingSpec(
2025-03-14T04:33:15.1556151Z             dim=sharding_dim,
2025-03-14T04:33:15.1556448Z             placements=[
2025-03-14T04:33:15.1556770Z                 "rank:0/cuda:0",
2025-03-14T04:33:15.1557073Z                 "rank:1/cuda:1",
2025-03-14T04:33:15.1557373Z                 "rank:2/cuda:2",
2025-03-14T04:33:15.1557671Z                 "rank:3/cuda:3",
2025-03-14T04:33:15.1557959Z             ],
2025-03-14T04:33:15.1558199Z         )
2025-03-14T04:33:15.1558451Z     >>> st.reshard(resharding_spec)
2025-03-14T04:33:15.1558791Z     >>> tensor = st.local_shards()[0].tensor
2025-03-14T04:33:15.1559191Z     >>> tensor
2025-03-14T04:33:15.1559491Z     tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2025-03-14T04:33:15.1559905Z     tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2025-03-14T04:33:15.1560312Z     tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2025-03-14T04:33:15.1560725Z     tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2025-03-14T04:33:15.1560986Z 
2025-03-14T04:33:15.1561260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.1561633Z 
2025-03-14T04:33:15.1642166Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12.
2025-03-14T04:33:15.1643164Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.1643617Z 
2025-03-14T04:33:15.1643857Z Representation of a sharding plan, describes how to shard a module
2025-03-14T04:33:15.1644469Z across hosts. `plan` is used to shard module parameters according to the spec provided,
2025-03-14T04:33:15.1645158Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2025-03-14T04:33:15.1645823Z layout of a module with a spec, and when to convert back to data parallel fashion.
2025-03-14T04:33:15.1646202Z 
2025-03-14T04:33:15.1646311Z Args:
2025-03-14T04:33:15.1646718Z     plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2025-03-14T04:33:15.1647293Z           :class:`torch.distributed._shard.sharder.Sharder`]):
2025-03-14T04:33:15.1647857Z         a dict describes how to shard a module, there're currently two ways to shard a module:
2025-03-14T04:33:15.1648513Z             1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2025-03-14T04:33:15.1649234Z                a parameter to a `ShardingSpec`.
2025-03-14T04:33:15.1649732Z             2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2025-03-14T04:33:15.1650235Z                to a `Sharder` object.
2025-03-14T04:33:15.1650788Z     output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2025-03-14T04:33:15.1651506Z         a dict specifies the layout of a module's output which produces a ShardedTensor,
2025-03-14T04:33:15.1652190Z         keyed by the name of module to ShardingSpec("" in key means the root module).
2025-03-14T04:33:15.1652699Z         Default: `None`
2025-03-14T04:33:15.1653134Z     return_local_tensor (List[str], optional): a list of string, each element enables
2025-03-14T04:33:15.1653765Z         a module's sharded output to be returned as a Tensor from its local shards to
2025-03-14T04:33:15.1654396Z         ensure further processing in a data parallel fashion. ("" in list means the
2025-03-14T04:33:15.1654873Z         root module).
2025-03-14T04:33:15.1655142Z         Default: None
2025-03-14T04:33:15.1655406Z Example:
2025-03-14T04:33:15.1655838Z   Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2025-03-14T04:33:15.1656537Z   want to convert the output of the second linear layer back to DDP, we can do it as follows:
2025-03-14T04:33:15.1656939Z 
2025-03-14T04:33:15.1657132Z     >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2025-03-14T04:33:15.1657552Z     >>> class MyModule(nn.Module):
2025-03-14T04:33:15.1657890Z     >>>     def __init__(self) -> None:
2025-03-14T04:33:15.1658228Z     >>>        super().__init__()
2025-03-14T04:33:15.1658595Z     >>>        self.fc1 = nn.Linear()
2025-03-14T04:33:15.1658931Z     >>>        self.gelu = nn.GELU()
2025-03-14T04:33:15.1659265Z     >>>        self.fc2 = nn.Linear()
2025-03-14T04:33:15.1659603Z     >>>        self.relu = nn.Linear()
2025-03-14T04:33:15.1659905Z     >>>
2025-03-14T04:33:15.1660162Z     >>>     def forward(self, input):
2025-03-14T04:33:15.1660566Z     >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2025-03-14T04:33:15.1660880Z 
2025-03-14T04:33:15.1660884Z 
2025-03-14T04:33:15.1661023Z     >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2025-03-14T04:33:15.1661402Z     >>> sharding_plan = ShardingPlan(
2025-03-14T04:33:15.1661729Z     >>>    plan={
2025-03-14T04:33:15.1661996Z     >>>        "fc1.weight": spec1,
2025-03-14T04:33:15.1662319Z     >>>        "fc2.weight": spec2
2025-03-14T04:33:15.1662628Z     >>>    },
2025-03-14T04:33:15.1662875Z     >>>    output_plan={
2025-03-14T04:33:15.1663160Z     >>>        "fc2": output_spec
2025-03-14T04:33:15.1663458Z     >>>    },
2025-03-14T04:33:15.1663713Z     >>>    return_local_tensor=["fc2"]
2025-03-14T04:33:15.1664025Z     >>> )
2025-03-14T04:33:15.1664147Z 
2025-03-14T04:33:15.1664416Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.1664787Z 
2025-03-14T04:33:15.2304839Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72.
2025-03-14T04:33:15.2305971Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2306372Z 
2025-03-14T04:33:15.2306508Z Run post-localSGD algorithm.
2025-03-14T04:33:15.2306703Z 
2025-03-14T04:33:15.2306958Z This DDP communication hook is used for running post-localSGD algorithm,
2025-03-14T04:33:15.2307494Z by combining with a model averaging component (e.g.,
2025-03-14T04:33:15.2308108Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2025-03-14T04:33:15.2308695Z that runs after the optimizer step.
2025-03-14T04:33:15.2308910Z 
2025-03-14T04:33:15.2309018Z Args:
2025-03-14T04:33:15.2309384Z     state (PostLocalSGDState): State information to run post-localSGD.
2025-03-14T04:33:15.2310015Z         Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2025-03-14T04:33:15.2310840Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-14T04:33:15.2311639Z         Note that since DDP comm hook only supports single process single device mode,
2025-03-14T04:33:15.2312174Z         only exactly one tensor is stored in this bucket.
2025-03-14T04:33:15.2312437Z 
2025-03-14T04:33:15.2312544Z Returns:
2025-03-14T04:33:15.2312980Z     Future handler of the communication, which updates the gradients in place.
2025-03-14T04:33:15.2313394Z 
2025-03-14T04:33:15.2313531Z Example::
2025-03-14T04:33:15.2313767Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.2314220Z     >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2025-03-14T04:33:15.2314733Z                               start_localSGD_iter=10)
2025-03-14T04:33:15.2315167Z     >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:15.2315818Z     >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2025-03-14T04:33:15.2316623Z     >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2025-03-14T04:33:15.2317099Z 
2025-03-14T04:33:15.2317355Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2317738Z 
2025-03-14T04:33:15.2349187Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342.
2025-03-14T04:33:15.2350323Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2350791Z 
2025-03-14T04:33:15.2350926Z Implement PowerSGD algorithm.
2025-03-14T04:33:15.2351128Z 
2025-03-14T04:33:15.2351375Z This DDP communication hook implements PowerSGD gradient compression
2025-03-14T04:33:15.2351986Z algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2025-03-14T04:33:15.2352594Z Once gradient tensors are aggregated across all workers, this hook applies
2025-03-14T04:33:15.2353055Z compression as follows:
2025-03-14T04:33:15.2353241Z 
2025-03-14T04:33:15.2353678Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2025-03-14T04:33:15.2354243Z 
2025-03-14T04:33:15.2354681Z     1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2025-03-14T04:33:15.2355217Z 
2025-03-14T04:33:15.2355791Z     1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2025-03-14T04:33:15.2356594Z 
2025-03-14T04:33:15.2356729Z 2. Handles uncompressed tensors:
2025-03-14T04:33:15.2356931Z 
2025-03-14T04:33:15.2357456Z     2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2025-03-14T04:33:15.2358131Z 
2025-03-14T04:33:15.2358491Z     2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2025-03-14T04:33:15.2358949Z 
2025-03-14T04:33:15.2359281Z 3. Handles the tensors that should be compressed by PowerSGD compression:
2025-03-14T04:33:15.2359631Z 
2025-03-14T04:33:15.2359890Z     3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2025-03-14T04:33:15.2360583Z     such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2025-03-14T04:33:15.2361009Z 
2025-03-14T04:33:15.2361176Z     3.2. Computes each P in Ps, which is equal to MQ;
2025-03-14T04:33:15.2361438Z 
2025-03-14T04:33:15.2361566Z     3.3. Allreduces Ps as a batch;
2025-03-14T04:33:15.2361771Z 
2025-03-14T04:33:15.2361907Z     3.4. Orthogonalizes each P in Ps;
2025-03-14T04:33:15.2362123Z 
2025-03-14T04:33:15.2362338Z     3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2025-03-14T04:33:15.2362649Z 
2025-03-14T04:33:15.2362777Z     3.6. Allreduces Qs as a batch;
2025-03-14T04:33:15.2362980Z 
2025-03-14T04:33:15.2363293Z     3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2025-03-14T04:33:15.2363702Z 
2025-03-14T04:33:15.2364125Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2025-03-14T04:33:15.2364941Z This not only gives the user more control over the tradeoff between speedup and accuracy,
2025-03-14T04:33:15.2365905Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2025-03-14T04:33:15.2366453Z 
2025-03-14T04:33:15.2366561Z Args:
2025-03-14T04:33:15.2367131Z     state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2025-03-14T04:33:15.2368047Z         To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2025-03-14T04:33:15.2368650Z         and ``min_compression_rate``.
2025-03-14T04:33:15.2369295Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-14T04:33:15.2370088Z         Note that since DDP comm hook only supports single process single device mode,
2025-03-14T04:33:15.2370607Z         only exactly one tensor is stored in this bucket.
2025-03-14T04:33:15.2370883Z 
2025-03-14T04:33:15.2370977Z Returns:
2025-03-14T04:33:15.2371363Z     Future handler of the communication, which updates the gradients in place.
2025-03-14T04:33:15.2371735Z 
2025-03-14T04:33:15.2371854Z Example::
2025-03-14T04:33:15.2372131Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.2372601Z     >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2025-03-14T04:33:15.2373178Z                               start_powerSGD_iter=10, min_compression_rate=0.5)
2025-03-14T04:33:15.2373647Z     >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2025-03-14T04:33:15.2373935Z 
2025-03-14T04:33:15.2374191Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2374578Z 
2025-03-14T04:33:15.2385269Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38.
2025-03-14T04:33:15.2386390Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2386792Z 
2025-03-14T04:33:15.2386989Z Averages parameters periodically after the warm-up stage.
2025-03-14T04:33:15.2387309Z 
2025-03-14T04:33:15.2387575Z This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-14T04:33:15.2388166Z by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2025-03-14T04:33:15.2388734Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2025-03-14T04:33:15.2389104Z 
2025-03-14T04:33:15.2389197Z Args:
2025-03-14T04:33:15.2389562Z     period (int): The number of steps per model averaging.
2025-03-14T04:33:15.2390143Z                   Usually the period should be greater than ``1`` to reduce the communication cost.
2025-03-14T04:33:15.2390682Z                   Otherwise, only DDP needs to be used.
2025-03-14T04:33:15.2391153Z     warmup_steps (int): The number of warm-up steps. During this stage,
2025-03-14T04:33:15.2391624Z                         model averaging is skipped.
2025-03-14T04:33:15.2392070Z     process_group: The process group to be used for all-reduce.
2025-03-14T04:33:15.2392541Z                    If ``None``, the default process group, which
2025-03-14T04:33:15.2393009Z                    is created by :func:`torch.distributed.init_process_group`,
2025-03-14T04:33:15.2393451Z                    will be used. (default: ``None``)
2025-03-14T04:33:15.2393699Z 
2025-03-14T04:33:15.2393800Z Example::
2025-03-14T04:33:15.2393943Z 
2025-03-14T04:33:15.2394073Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.2394425Z     >>> import torch
2025-03-14T04:33:15.2394718Z     >>> import torch.distributed as dist
2025-03-14T04:33:15.2395269Z     >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2025-03-14T04:33:15.2395982Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-14T04:33:15.2396492Z     >>> import torch.nn as nn
2025-03-14T04:33:15.2396781Z     >>>
2025-03-14T04:33:15.2397134Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-14T04:33:15.2397587Z     >>> torch.cuda.set_device(rank)
2025-03-14T04:33:15.2397943Z     >>> module = nn.Linear(1, 1, bias=False).cuda()
2025-03-14T04:33:15.2398347Z     >>> model = nn.parallel.DistributedDataParallel(
2025-03-14T04:33:15.2398778Z     >>>    module, device_ids=[rank], output_device=rank
2025-03-14T04:33:15.2399211Z     >>> )
2025-03-14T04:33:15.2399505Z     >>> # Register a post-localSGD communication hook.
2025-03-14T04:33:15.2400246Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-14T04:33:15.2401117Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:15.2401636Z     >>>
2025-03-14T04:33:15.2402085Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-14T04:33:15.2403136Z     >>> # After 100 steps, run model averaging every 4 steps.
2025-03-14T04:33:15.2403474Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-14T04:33:15.2403729Z     >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-14T04:33:15.2403920Z     >>> for step in range(0, 200):
2025-03-14T04:33:15.2404035Z     >>>    optimizer.zero_grad()
2025-03-14T04:33:15.2404172Z     >>>    loss = loss_fn(output, labels)
2025-03-14T04:33:15.2404280Z     >>>    loss.backward()
2025-03-14T04:33:15.2404406Z     >>>    optimizer.step()
2025-03-14T04:33:15.2404611Z     >>>    # Will average model parameters globally every 4 steps. Thus,
2025-03-14T04:33:15.2404838Z     >>>    # inter-node communication only occurs every 4 iterations after
2025-03-14T04:33:15.2404971Z     >>>    # the initial ``warmup_steps`` period.
2025-03-14T04:33:15.2405156Z     >>>    averager.average_parameters(model.parameters())
2025-03-14T04:33:15.2405162Z 
2025-03-14T04:33:15.2405420Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2405425Z 
2025-03-14T04:33:15.2406320Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19.
2025-03-14T04:33:15.2406602Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2406606Z 
2025-03-14T04:33:15.2406950Z Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2025-03-14T04:33:15.2406955Z 
2025-03-14T04:33:15.2407315Z Process groups of different sizes are organized in a hierarchy, and they average parameters
2025-03-14T04:33:15.2407526Z by using different periods concurrently after the warm-up stage.
2025-03-14T04:33:15.2407944Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2025-03-14T04:33:15.2408280Z that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2025-03-14T04:33:15.2408606Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2025-03-14T04:33:15.2408956Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2025-03-14T04:33:15.2409278Z Similarly, the process groups within this class do not have such an intra-machine process
2025-03-14T04:33:15.2409558Z subgroup, which should be embedded by the post-local SGD communication hook instead.
2025-03-14T04:33:15.2409563Z 
2025-03-14T04:33:15.2409671Z Args:
2025-03-14T04:33:15.2409936Z     period_group_size_dict: An ordered dict mapping keys of model averaging period to
2025-03-14T04:33:15.2410155Z                             process group size, used for initializing process groups of
2025-03-14T04:33:15.2410384Z                             different sizes in a hierarchy to average parameters concurrently.
2025-03-14T04:33:15.2410617Z                             Particularly, at each iteration, there will be at most a single
2025-03-14T04:33:15.2410925Z                             process group that runs averaging -- the period of such group should
2025-03-14T04:33:15.2411159Z                             have the largest period which the current step can be divided by.
2025-03-14T04:33:15.2411336Z                             For example, if the dict has three keys: 2, 4, and 8,
2025-03-14T04:33:15.2411562Z                             then this means totally three process groups will be created to
2025-03-14T04:33:15.2411776Z                             average parameters every 2, 4, and 8 iterations, respectively.
2025-03-14T04:33:15.2411990Z                             At the 4th iteration, only the second process group will run
2025-03-14T04:33:15.2412173Z                             averaging, because the first process group should be a
2025-03-14T04:33:15.2412420Z                             subset of the second process group, and no need to execute the first
2025-03-14T04:33:15.2412554Z                             process group redundantly.
2025-03-14T04:33:15.2412777Z                             On the other hand, the third process group can only be triggered
2025-03-14T04:33:15.2413004Z                             every 8 iterations, so it will not be triggered at the 4th iteration.
2025-03-14T04:33:15.2413353Z     warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2025-03-14T04:33:15.2413798Z     process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2025-03-14T04:33:15.2413992Z                                             If ``None``, the default process group, which is created
2025-03-14T04:33:15.2414205Z                                             by :func:`torch.distributed.init_process_group`, will be used.
2025-03-14T04:33:15.2414345Z                                             (default: ``None``)
2025-03-14T04:33:15.2414350Z 
2025-03-14T04:33:15.2414453Z Example::
2025-03-14T04:33:15.2414590Z     >>> # xdoctest: +SKIP('undefined rank')
2025-03-14T04:33:15.2414719Z     >>> from collections import OrderedDict
2025-03-14T04:33:15.2414830Z     >>> import torch
2025-03-14T04:33:15.2414952Z     >>> import torch.distributed as dist
2025-03-14T04:33:15.2415243Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-14T04:33:15.2415355Z     >>>     PostLocalSGDState,
2025-03-14T04:33:15.2415477Z     >>>     post_localSGD_hook,
2025-03-14T04:33:15.2415570Z     >>> )
2025-03-14T04:33:15.2415988Z     >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2025-03-14T04:33:15.2416098Z     >>> import torch.nn as nn
2025-03-14T04:33:15.2416204Z     >>>
2025-03-14T04:33:15.2416386Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-14T04:33:15.2416515Z     >>> torch.cuda.set_device(rank)
2025-03-14T04:33:15.2416656Z     >>> module = nn.Linear(1, 1, bias=False).to(rank)
2025-03-14T04:33:15.2416831Z     >>> model = nn.parallel.DistributedDataParallel(
2025-03-14T04:33:15.2416983Z     >>>    module, device_ids=[rank], output_device=rank
2025-03-14T04:33:15.2417089Z     >>> )
2025-03-14T04:33:15.2417241Z     >>> # Register a post-localSGD communication hook.
2025-03-14T04:33:15.2417537Z     >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2025-03-14T04:33:15.2417665Z     >>> subgroup, _ = dist.new_subgroups()
2025-03-14T04:33:15.2417996Z     >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2025-03-14T04:33:15.2418165Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:15.2418275Z     >>>
2025-03-14T04:33:15.2418560Z     >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2025-03-14T04:33:15.2418704Z     >>> # the 16 processes every 16 iterations.
2025-03-14T04:33:15.2418895Z     >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2025-03-14T04:33:15.2419170Z     >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2025-03-14T04:33:15.2419513Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-14T04:33:15.2419799Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-14T04:33:15.2419956Z     >>> # After 100 steps, run model averaging at two levels.
2025-03-14T04:33:15.2420082Z     >>> for step in range(0, 200):
2025-03-14T04:33:15.2420197Z     >>>    optimizer.zero_grad()
2025-03-14T04:33:15.2420329Z     >>>    loss = loss_fn(output, labels)
2025-03-14T04:33:15.2420436Z     >>>    loss.backward()
2025-03-14T04:33:15.2420559Z     >>>    optimizer.step()
2025-03-14T04:33:15.2420721Z     >>>    # Average parameters after ``optimizer.step()``.
2025-03-14T04:33:15.2421022Z     >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2025-03-14T04:33:15.2421188Z     >>>    averager.average_parameters(model.parameters())
2025-03-14T04:33:15.2421195Z 
2025-03-14T04:33:15.2421312Z .. warning ::
2025-03-14T04:33:15.2421567Z     The last group size in the dict must be the size of the provided ``process_group``,
2025-03-14T04:33:15.2421843Z     which indicates model averaging at the highest level of the hierarchy.
2025-03-14T04:33:15.2433695Z     If ``process_group`` is not provided, then the last group size should be equal to the world size.
2025-03-14T04:33:15.2433718Z 
2025-03-14T04:33:15.2433884Z .. warning ::
2025-03-14T04:33:15.2434176Z     `HierarchicalModelAverager` is experimental and subject to change.
2025-03-14T04:33:15.2434181Z 
2025-03-14T04:33:15.2434445Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2434451Z 
2025-03-14T04:33:15.2668708Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40.
2025-03-14T04:33:15.2669819Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2670211Z 
2025-03-14T04:33:15.2670525Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2025-03-14T04:33:15.2671294Z on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2025-03-14T04:33:15.2672001Z 
2025-03-14T04:33:15.2672215Z . N.B. Intended to be used with DynamicMetaLoadPlanner
2025-03-14T04:33:15.2672497Z 
2025-03-14T04:33:15.2672621Z .. warning::
2025-03-14T04:33:15.2673061Z     Current implementation only supports loading Tensors.
2025-03-14T04:33:15.2673353Z 
2025-03-14T04:33:15.2673495Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2673837Z >>> sd = {"mode": model}
2025-03-14T04:33:15.2674121Z >>> dcp.load(
2025-03-14T04:33:15.2674349Z >>>    sd,
2025-03-14T04:33:15.2674650Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-14T04:33:15.2675058Z >>>    planner=DynamicMetaLoadPlanner(),
2025-03-14T04:33:15.2675426Z >>>    checkpoint_id="path_to_model.pt"
2025-03-14T04:33:15.2675761Z >>> )
2025-03-14T04:33:15.2675902Z 
2025-03-14T04:33:15.2676161Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2676551Z 
2025-03-14T04:33:15.2677243Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151.
2025-03-14T04:33:15.2678305Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2678702Z 
2025-03-14T04:33:15.2679067Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2025-03-14T04:33:15.2679935Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2025-03-14T04:33:15.2680510Z metadata file, like Torch Save files.
2025-03-14T04:33:15.2680746Z 
2025-03-14T04:33:15.2680935Z . N.B. Intended to be used with BroadcastingTorchSaveReader
2025-03-14T04:33:15.2681243Z 
2025-03-14T04:33:15.2681459Z .. warning::
2025-03-14T04:33:15.2681787Z     Current implementation only supports loading Tensors.
2025-03-14T04:33:15.2682088Z 
2025-03-14T04:33:15.2682212Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2682549Z >>> sd = {"mode": model}
2025-03-14T04:33:15.2682826Z >>> dcp.load(
2025-03-14T04:33:15.2683068Z >>>    sd,
2025-03-14T04:33:15.2683366Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-14T04:33:15.2683779Z >>>    planner=DynamicMetaLoadPlanner(),
2025-03-14T04:33:15.2684147Z >>>    checkpoint_id="path_to_model.pt"
2025-03-14T04:33:15.2684473Z >>> )
2025-03-14T04:33:15.2684596Z 
2025-03-14T04:33:15.2684867Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2685238Z 
2025-03-14T04:33:15.2721783Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221.
2025-03-14T04:33:15.2722880Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2723274Z 
2025-03-14T04:33:15.2723490Z Load a state_dict in conjunction with FSDP sharded optimizer state.
2025-03-14T04:33:15.2723914Z 
2025-03-14T04:33:15.2724087Z This is the current recommended way to checkpoint FSDP.
2025-03-14T04:33:15.2724485Z >>> # xdoctest: +SKIP
2025-03-14T04:33:15.2724826Z >>> import torch.distributed.checkpoint as dist_cp
2025-03-14T04:33:15.2725203Z >>> # Save
2025-03-14T04:33:15.2725455Z >>> model: torch.nn.Model
2025-03-14T04:33:15.2725766Z >>> optim_params = model.parameters()
2025-03-14T04:33:15.2726150Z >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2025-03-14T04:33:15.2726510Z >>> # Save
2025-03-14T04:33:15.2726869Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2025-03-14T04:33:15.2727304Z >>>     state_dict = {
2025-03-14T04:33:15.2727640Z >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2025-03-14T04:33:15.2728043Z >>>         "model": model.state_dict()
2025-03-14T04:33:15.2728371Z >>>     }
2025-03-14T04:33:15.2728622Z >>>     dist_cp.save_state_dict(
2025-03-14T04:33:15.2728947Z >>>         state_dict=optim_state,
2025-03-14T04:33:15.2729356Z >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2025-03-14T04:33:15.2729803Z >>>         planner=dist_cp.DefaultSavePlanner(),
2025-03-14T04:33:15.2730155Z >>>     )
2025-03-14T04:33:15.2730382Z >>>
2025-03-14T04:33:15.2730605Z >>> # Load
2025-03-14T04:33:15.2731013Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2025-03-14T04:33:15.2731509Z >>>     model_state_dict = model_tp.state_dict()
2025-03-14T04:33:15.2731870Z >>>     checkpoint = {
2025-03-14T04:33:15.2732161Z >>>         "model": model_state_dict
2025-03-14T04:33:15.2732477Z >>>     }
2025-03-14T04:33:15.2732713Z >>>     dist_cp.load_state_dict(
2025-03-14T04:33:15.2733037Z >>>         state_dict=checkpoint,
2025-03-14T04:33:15.2733447Z >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2025-03-14T04:33:15.2733912Z >>>         planner=dist_cp.DefaultLoadPlanner(),
2025-03-14T04:33:15.2734256Z >>>     )
2025-03-14T04:33:15.2734556Z >>>     model.load_state_dict(checkpoint["model_state"])
2025-03-14T04:33:15.2734920Z >>>
2025-03-14T04:33:15.2735229Z >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2025-03-14T04:33:15.2735638Z >>>         model_state_dict,
2025-03-14T04:33:15.2735957Z >>>         optimizer_key="optimizer",
2025-03-14T04:33:15.2736372Z >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2025-03-14T04:33:15.2736768Z >>>     )
2025-03-14T04:33:15.2736996Z >>>
2025-03-14T04:33:15.2737279Z >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2025-03-14T04:33:15.2737686Z >>>        model, optim, optim_state["optimizer"]
2025-03-14T04:33:15.2738035Z >>>     )
2025-03-14T04:33:15.2738244Z >>>
2025-03-14T04:33:15.2738503Z >>>     optim.load_state_dict(flattened_osd)
2025-03-14T04:33:15.2738751Z 
2025-03-14T04:33:15.2739039Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2739460Z 
2025-03-14T04:33:15.2744877Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=113.
2025-03-14T04:33:15.2745867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2746247Z 
2025-03-14T04:33:15.2746556Z Abstract class defining the protocol used by save_state_dict to plan the save process.
2025-03-14T04:33:15.2746962Z 
2025-03-14T04:33:15.2747277Z SavePlanners are stateful objects that can be used to customize the whole save process.
2025-03-14T04:33:15.2747689Z 
2025-03-14T04:33:15.2747987Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-14T04:33:15.2748517Z will be visible to the whole process.
2025-03-14T04:33:15.2748898Z 
2025-03-14T04:33:15.2749204Z A planner subclass can expect the following sequence of calls during save_state_dict:
2025-03-14T04:33:15.2749610Z 
2025-03-14T04:33:15.2749755Z 1) set_up_planner - called on all ranks.
2025-03-14T04:33:15.2750128Z     Signals the start of a checkpoint save.
2025-03-14T04:33:15.2750453Z 
2025-03-14T04:33:15.2750601Z 2) create_local_plan - called on all ranks.
2025-03-14T04:33:15.2751122Z     Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2025-03-14T04:33:15.2751545Z 
2025-03-14T04:33:15.2751773Z 3) create_global_plan - called on the coordinator rank only.
2025-03-14T04:33:15.2752379Z     Takes the SavePlan from all ranks and make any global decision.
2025-03-14T04:33:15.2752692Z 
2025-03-14T04:33:15.2752830Z 4) finish_plan - called on all ranks.
2025-03-14T04:33:15.2753277Z     This gives each rank a chance to adjust to global planning decisions.
2025-03-14T04:33:15.2753612Z 
2025-03-14T04:33:15.2753788Z 5) resolve_data - called multiple times on each rank
2025-03-14T04:33:15.2754274Z     Lookups a value on the `state_dict` for the storage layer to write.
2025-03-14T04:33:15.2754600Z 
2025-03-14T04:33:15.2754923Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2025-03-14T04:33:15.2755549Z most changes can be expressed by changes in a single method.
2025-03-14T04:33:15.2755845Z 
2025-03-14T04:33:15.2755989Z There are 3 usual patterns of extension:
2025-03-14T04:33:15.2756217Z 
2025-03-14T04:33:15.2756491Z Rewriting state_dict. This is the simplest way to extend the save process as it
2025-03-14T04:33:15.2757162Z doesn't requite understanding the intrincacies of how SavePlan works:
2025-03-14T04:33:15.2757506Z 
2025-03-14T04:33:15.2757646Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2758025Z >>> class RenamePlanner(DefaultSavePlanner):
2025-03-14T04:33:15.2758370Z >>>     def set_up_planner(
2025-03-14T04:33:15.2758657Z >>>         self,
2025-03-14T04:33:15.2758932Z >>>         state_dict: STATE_DICT_TYPE,
2025-03-14T04:33:15.2759362Z >>>         storage_meta: Optional[StorageMeta],
2025-03-14T04:33:15.2759733Z >>>         is_coordinator: bool,
2025-03-14T04:33:15.2760041Z >>>     ) -> None:
2025-03-14T04:33:15.2760314Z >>> # prefix all keys with `foo_``
2025-03-14T04:33:15.2760833Z >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2025-03-14T04:33:15.2761252Z 
2025-03-14T04:33:15.2761584Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2025-03-14T04:33:15.2762045Z 
2025-03-14T04:33:15.2762168Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2762537Z >>> class FP16Planner(DefaultSavePlanner):
2025-03-14T04:33:15.2762900Z >>>     def create_local_plan(self):
2025-03-14T04:33:15.2763253Z >>>         plan = super().create_local_plan()
2025-03-14T04:33:15.2763604Z >>>         for p in plan:
2025-03-14T04:33:15.2763931Z >>>             if p.tensor_data is not None:
2025-03-14T04:33:15.2764339Z >>>                 p.tensor_data.properties.dtype = torch.float16
2025-03-14T04:33:15.2764827Z >>>         return plan
2025-03-14T04:33:15.2765095Z >>>
2025-03-14T04:33:15.2765350Z >>>     def resolve_data(self, write_item):
2025-03-14T04:33:15.2765711Z >>>         item = super().resolve_data(write_item)
2025-03-14T04:33:15.2766243Z >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2025-03-14T04:33:15.2766649Z 
2025-03-14T04:33:15.2766991Z Using the global planning step to make central decisions that can't be made individually by each rank
2025-03-14T04:33:15.2767465Z 
2025-03-14T04:33:15.2767585Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2767941Z >>> from itertools import zip_longest
2025-03-14T04:33:15.2768286Z >>> from dataclasses import replace
2025-03-14T04:33:15.2768687Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2025-03-14T04:33:15.2769259Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
2025-03-14T04:33:15.2769799Z >>> # This sample doesn't handle ShardedTensors
2025-03-14T04:33:15.2770191Z >>>     def create_global_plan(self, all_plans):
2025-03-14T04:33:15.2770607Z >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2025-03-14T04:33:15.2771033Z >>>         items_per_rank = [
2025-03-14T04:33:15.2771381Z >>>             [item for item in items if item is not None]
2025-03-14T04:33:15.2771810Z >>>             for items in zip(*zip_longest(*iters), strict=True)
2025-03-14T04:33:15.2772191Z >>>         ]
2025-03-14T04:33:15.2772440Z >>>         all_plans = [
2025-03-14T04:33:15.2772749Z >>>             replace(plan, items=items)
2025-03-14T04:33:15.2773179Z >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2025-03-14T04:33:15.2773585Z >>>         ]
2025-03-14T04:33:15.2773876Z >>>         return super().create_global_plan(all_plans)
2025-03-14T04:33:15.2774142Z 
2025-03-14T04:33:15.2774407Z Finally, some planners need to save additional metadata in the checkpoint, this is
2025-03-14T04:33:15.2775082Z accomplished by having each rank contribute their data items in the local plan and
2025-03-14T04:33:15.2775599Z the global planner aggregate them:
2025-03-14T04:33:15.2775825Z 
2025-03-14T04:33:15.2775949Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2776344Z >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2025-03-14T04:33:15.2776762Z >>>     def create_local_plan(self) -> SavePlan:
2025-03-14T04:33:15.2777139Z >>>         plan = super().create_local_plan()
2025-03-14T04:33:15.2777582Z >>>         return replace(plan, planner_data="per-rank-data")
2025-03-14T04:33:15.2777954Z >>>
2025-03-14T04:33:15.2778383Z >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2025-03-14T04:33:15.2779013Z >>>         global_plan, metadata = super().create_global_plan(all_plans)
2025-03-14T04:33:15.2779506Z >>>         merged_data = [p.planner_data for p in global_plan]
2025-03-14T04:33:15.2779970Z >>>         metadata = replace(metadata, planner_data=merged_data)
2025-03-14T04:33:15.2780390Z >>>         return global_plan, metadata
2025-03-14T04:33:15.2780609Z 
2025-03-14T04:33:15.2780883Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2781253Z 
2025-03-14T04:33:15.2781862Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=293.
2025-03-14T04:33:15.2782838Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2783218Z 
2025-03-14T04:33:15.2783522Z Abstract class defining the protocol used by load_state_dict to plan the load process.
2025-03-14T04:33:15.2783924Z 
2025-03-14T04:33:15.2784230Z LoadPlanner are stateful objects that can be used to customize the whole load process.
2025-03-14T04:33:15.2784633Z 
2025-03-14T04:33:15.2784936Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-14T04:33:15.2785460Z will be visible to the whole process.
2025-03-14T04:33:15.2785708Z 
2025-03-14T04:33:15.2786048Z A planner subclass can expect the following sequence of calls during load_state_dict:
2025-03-14T04:33:15.2786443Z 
2025-03-14T04:33:15.2786589Z 1) set_up_planner - called on all ranks.
2025-03-14T04:33:15.2786961Z     Signals the start of loading a checkpoint.
2025-03-14T04:33:15.2787200Z 
2025-03-14T04:33:15.2787341Z 2) create_local_plan - called on all ranks.
2025-03-14T04:33:15.2787871Z     Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2025-03-14T04:33:15.2788271Z 
2025-03-14T04:33:15.2788472Z 3) create_global_plan - called on the coordinator rank only.
2025-03-14T04:33:15.2788972Z     Takes the LoadPlan from all ranks and make any global decision.
2025-03-14T04:33:15.2789280Z 
2025-03-14T04:33:15.2789439Z 4) load_bytes - called multiple times on each rank
2025-03-14T04:33:15.2789877Z     This is called once per non-tensor value in state_dict.
2025-03-14T04:33:15.2790155Z 
2025-03-14T04:33:15.2790394Z 5) resolve_tensor and commit_tensor - called multiple times on each rank
2025-03-14T04:33:15.2790929Z     They are called in pair for each Tensor value in state_dict.
2025-03-14T04:33:15.2791251Z 
2025-03-14T04:33:15.2791564Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2025-03-14T04:33:15.2792177Z most changes can be expressed by changes in a single method.
2025-03-14T04:33:15.2792475Z 
2025-03-14T04:33:15.2792622Z There are two usual patterns of extension:
2025-03-14T04:33:15.2792861Z 
2025-03-14T04:33:15.2793129Z Rewriting state_dict. This is the simplest way to extend the load process as it
2025-03-14T04:33:15.2793763Z doesn't requite understanding the intrincacies of how LoadPlan works. We need
2025-03-14T04:33:15.2794372Z to keep a reference to the original state_dict as load happens in place so
2025-03-14T04:33:15.2794854Z we need to be able to perform it in place
2025-03-14T04:33:15.2795080Z 
2025-03-14T04:33:15.2795200Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2795572Z >>> class RenamePlanner(DefaultLoadPlanner):
2025-03-14T04:33:15.2795927Z >>>     def set_up_planner(
2025-03-14T04:33:15.2796209Z >>>         self,
2025-03-14T04:33:15.2796478Z >>>         state_dict: STATE_DICT_TYPE,
2025-03-14T04:33:15.2796810Z >>>         metadata: Metadata,
2025-03-14T04:33:15.2797121Z >>>         is_coordinator: bool,
2025-03-14T04:33:15.2797428Z >>>     ) -> None:
2025-03-14T04:33:15.2797733Z >>>         self.original_state_dict = state_dict
2025-03-14T04:33:15.2798188Z >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2025-03-14T04:33:15.2798577Z >>>
2025-03-14T04:33:15.2798843Z >>>         if self.flatten_sharded_tensors:
2025-03-14T04:33:15.2799318Z >>>             state_dict = _flatten_sharded_tensors(state_dict)
2025-03-14T04:33:15.2799686Z >>>
2025-03-14T04:33:15.2799929Z >>>         if self.flatten_state_dict:
2025-03-14T04:33:15.2800348Z >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2025-03-14T04:33:15.2800749Z >>>
2025-03-14T04:33:15.2800996Z >>>         self.state_dict = state_dict
2025-03-14T04:33:15.2801319Z >>>         self.metadata = metadata
2025-03-14T04:33:15.2801665Z >>>         self.is_coordinator = is_coordinator
2025-03-14T04:33:15.2801996Z >>>
2025-03-14T04:33:15.2802246Z >>>     def load_bytes(self, read_item, value):
2025-03-14T04:33:15.2802601Z >>> # Remove the "foo_" prefix
2025-03-14T04:33:15.2803122Z >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2025-03-14T04:33:15.2803564Z 
2025-03-14T04:33:15.2803568Z 
2025-03-14T04:33:15.2803831Z Modifying resolve_tensor and commit_tensor to handle load time transformation.
2025-03-14T04:33:15.2804221Z 
2025-03-14T04:33:15.2804342Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.2804730Z >>> class MetaModelMaterialize(DefaultSavePlanner):
2025-03-14T04:33:15.2805129Z >>>     def resolve_tensor(self, read_item):
2025-03-14T04:33:15.2805506Z >>>         tensor = super().resolve_tensor(read_item)
2025-03-14T04:33:15.2805990Z >>>         return torch.empty_like(tensor, device="cpu")
2025-03-14T04:33:15.2806346Z >>>
2025-03-14T04:33:15.2806608Z >>>     def commit_tensor(self, read_item, tensor):
2025-03-14T04:33:15.2807025Z >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2025-03-14T04:33:15.2807291Z 
2025-03-14T04:33:15.2807557Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2807926Z 
2025-03-14T04:33:15.2902326Z msg = Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1124.
2025-03-14T04:33:15.2903321Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2903716Z 
2025-03-14T04:33:15.2903886Z Return the model state_dict and optimizers state_dict.
2025-03-14T04:33:15.2904163Z 
2025-03-14T04:33:15.2904408Z ``get_state_dict`` can process any module that is parallelized by PyTorch
2025-03-14T04:33:15.2905023Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
2025-03-14T04:33:15.2905652Z combination of these parallelisms. The main functions of ``get_state_dict``
2025-03-14T04:33:15.2906370Z are: 1.) returning a model and optimizer state_dict that can be resharded
2025-03-14T04:33:15.2906928Z with a different number of trainers and/or different parallelisms.
2025-03-14T04:33:15.2907519Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
2025-03-14T04:33:15.2907994Z these APIs.
2025-03-14T04:33:15.2908267Z 3.) sanity checking the result state_dict.
2025-03-14T04:33:15.2908498Z 
2025-03-14T04:33:15.2908724Z The keys of the result state dictionary are the canonical FQNs (Fully
2025-03-14T04:33:15.2909294Z Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
2025-03-14T04:33:15.2909896Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a
2025-03-14T04:33:15.2910474Z parameter is the FQN returned by ``module.named_parameters()`` or
2025-03-14T04:33:15.2911023Z ``module.named_buffers()`` when the module is not distributed by any
2025-03-14T04:33:15.2911619Z parallelisms. Since the optimizer internally uses parameter IDs to represent
2025-03-14T04:33:15.2912231Z a parameter, there will be a conversion from the parameter IDs to the
2025-03-14T04:33:15.2912701Z canonical FQNs when calling this API.
2025-03-14T04:33:15.2912922Z 
2025-03-14T04:33:15.2913206Z ``get_state_dict`` can also process a module that is not parallelized. In
2025-03-14T04:33:15.2913783Z such a case, ``get_state_dict`` only performs one function -- converting the
2025-03-14T04:33:15.2914289Z optimizer parameter IDs to the canonical FQNs.
2025-03-14T04:33:15.2914548Z 
2025-03-14T04:33:15.2914647Z Example:
2025-03-14T04:33:15.2914897Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.2915188Z     >>> import torch
2025-03-14T04:33:15.2915611Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:15.2916196Z     >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-14T04:33:15.2916759Z     >>> from torch.distributed.checkpoint.state_dict import get_state_dict
2025-03-14T04:33:15.2917122Z 
2025-03-14T04:33:15.2917260Z     >>> fsdp_model = FSDP(copy.deepcopy(model))
2025-03-14T04:33:15.2917707Z     >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-14T04:33:15.2918150Z     >>> ddp_model = DDP(copy.deepcopy(model))
2025-03-14T04:33:15.2918584Z     >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-14T04:33:15.2918897Z 
2025-03-14T04:33:15.2918901Z 
2025-03-14T04:33:15.2919200Z     >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
2025-03-14T04:33:15.2919746Z     >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
2025-03-14T04:33:15.2920158Z     ...     fsdp_model, fsdp_optim
2025-03-14T04:33:15.2920463Z     ... )
2025-03-14T04:33:15.2920587Z 
2025-03-14T04:33:15.2920870Z     >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
2025-03-14T04:33:15.2921382Z     >>> # the asserts will fail.
2025-03-14T04:33:15.2921719Z     >>> assert ddp_state_dict == fsdp_state_dict
2025-03-14T04:33:15.2922125Z     >>> assert ddp_optim_state == fsdp_optim_state_dict
2025-03-14T04:33:15.2922383Z 
2025-03-14T04:33:15.2922388Z 
2025-03-14T04:33:15.2922491Z Args:
2025-03-14T04:33:15.2922767Z     model (nn.Module): the nn.Module to the model.
2025-03-14T04:33:15.2923225Z     optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
2025-03-14T04:33:15.2923705Z         The optimizers that are used to optimize ``model``.
2025-03-14T04:33:15.2924268Z     submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
2025-03-14T04:33:15.2924796Z         that belong to the submodules.
2025-03-14T04:33:15.2925204Z     options (StateDictOptions): the options to control how
2025-03-14T04:33:15.2925716Z         model state_dict and optimizer state_dict should be returned. See
2025-03-14T04:33:15.2926166Z         `StateDictOptions` for the details.
2025-03-14T04:33:15.2926416Z 
2025-03-14T04:33:15.2926509Z Returns:
2025-03-14T04:33:15.2926845Z     ``Tuple`` that contain model state_dict and optimizer state_dict.
2025-03-14T04:33:15.2927207Z 
2025-03-14T04:33:15.2927441Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
2025-03-14T04:33:15.2927801Z 
2025-03-14T04:33:15.2928057Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2928440Z 
2025-03-14T04:33:15.2931748Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=62.
2025-03-14T04:33:15.2932760Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2933140Z 
2025-03-14T04:33:15.2933360Z Load a checkpoint into a distributed state dict in SPMD style.
2025-03-14T04:33:15.2933667Z 
2025-03-14T04:33:15.2933908Z Each rank must have the same keys in their ``state_dict`` provided to this
2025-03-14T04:33:15.2934483Z API. Mismatched keys may result in hangs or errors. If unsure, you can use
2025-03-14T04:33:15.2935070Z the ``utils._assert_same_keys`` API to check (but may incur communication
2025-03-14T04:33:15.2935518Z costs).
2025-03-14T04:33:15.2935659Z 
2025-03-14T04:33:15.2935846Z Each rank will try to read the least amount of data necessary
2025-03-14T04:33:15.2936387Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor`
2025-03-14T04:33:15.2937056Z or :class:`DTensor` instances, each rank only reads data for their local shards.
2025-03-14T04:33:15.2937461Z 
2025-03-14T04:33:15.2937741Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-14T04:33:15.2938391Z load will first call ``state_dict`` before attempting deserialization, followed by
2025-03-14T04:33:15.2938944Z ``load_state_dict`` once the deserialization is complete.
2025-03-14T04:33:15.2939495Z For each non-``Stateful`` object, load will deserailize the object, and then replace
2025-03-14T04:33:15.2940027Z it in the ``state_dict`` with the deserialized object.
2025-03-14T04:33:15.2940305Z 
2025-03-14T04:33:15.2940419Z .. warning::
2025-03-14T04:33:15.2940735Z     All tensors in ``state_dict`` must be allocated on their
2025-03-14T04:33:15.2941194Z     destination device *prior to* calling this function.
2025-03-14T04:33:15.2941481Z 
2025-03-14T04:33:15.2941713Z     All non-tensor data is loaded using `torch.load()` and modified in place
2025-03-14T04:33:15.2942165Z     on state_dict.
2025-03-14T04:33:15.2942331Z 
2025-03-14T04:33:15.2942427Z .. warning::
2025-03-14T04:33:15.2942783Z     Users must call `load_state_dict` on the root module to ensure load
2025-03-14T04:33:15.2943299Z     pos-processing and non-tensor data properly propagates.
2025-03-14T04:33:15.2943605Z 
2025-03-14T04:33:15.2943696Z .. note:
2025-03-14T04:33:15.2944059Z     If no process group is initialized, this function will assume the intent
2025-03-14T04:33:15.2944670Z     is to load a checkpoint into the local process. This can be useful in the
2025-03-14T04:33:15.2945302Z     case of local inference, and when using regular Tensors (as opposed to DTensor
2025-03-14T04:33:15.2945779Z      or ShardedTensor)
2025-03-14T04:33:15.2945944Z 
2025-03-14T04:33:15.2946051Z .. note:
2025-03-14T04:33:15.2946331Z     Rank 0 is assumed to be the coordinator rank.
2025-03-14T04:33:15.2946581Z 
2025-03-14T04:33:15.2946684Z Args:
2025-03-14T04:33:15.2947034Z     state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
2025-03-14T04:33:15.2947523Z     checkpoint_id (Union[str, os.PathLike, None]):
2025-03-14T04:33:15.2948004Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-14T04:33:15.2948545Z         depends on the storage. It can be a path to a folder or to a file.
2025-03-14T04:33:15.2949277Z         It can also be a key if the storage is a key-value store.
2025-03-14T04:33:15.2949684Z         (Default: ``None``)
2025-03-14T04:33:15.2950020Z     storage_reader (Optional[StorageReader]):
2025-03-14T04:33:15.2950474Z         Instance of StorageWriter used to perform reads. If this is not
2025-03-14T04:33:15.2951096Z         specified, DCP will automatically infer the reader based on the
2025-03-14T04:33:15.2951631Z         checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-14T04:33:15.2952079Z         be raised. (Default: ``None``)
2025-03-14T04:33:15.2952428Z     planner (Optional[LoadPlanner]):
2025-03-14T04:33:15.2952866Z         Instance of LoadPlanner. If this is not specificed, the default
2025-03-14T04:33:15.2953328Z         planner will be used. (Default: ``None``)
2025-03-14T04:33:15.2953714Z     process_group (Optional[ProcessGroup]):
2025-03-14T04:33:15.2954161Z         ProcessGroup to be used for cross-rank synchronization.
2025-03-14T04:33:15.2954574Z         (Default: ``None``)
2025-03-14T04:33:15.2954983Z     no_dist (bool): If ``True``, this function will assume the intent is to load
2025-03-14T04:33:15.2955588Z         a checkpoint without using cross-rank synchronization. (Default: ``False``)
2025-03-14T04:33:15.2956073Z Returns:
2025-03-14T04:33:15.2956323Z     None.
2025-03-14T04:33:15.2956515Z 
2025-03-14T04:33:15.2956631Z Examples
2025-03-14T04:33:15.2956879Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.2957234Z     >>> my_model = MyModule()
2025-03-14T04:33:15.2957580Z     >>> optimizer = Adagrad(my_model.parameters())
2025-03-14T04:33:15.2957977Z     >>> model_state_dict = my_model.state_dict()
2025-03-14T04:33:15.2958547Z     >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
2025-03-14T04:33:15.2959003Z     ...     "/checkpoint/1"
2025-03-14T04:33:15.2959381Z     ... )
2025-03-14T04:33:15.2959526Z 
2025-03-14T04:33:15.2959693Z     >>> torch.distributed.checkpoint.load_state_dict(
2025-03-14T04:33:15.2960101Z     >>>     state_dict=model_state_dict,
2025-03-14T04:33:15.2960461Z     >>>     storage_reader=fs_storage_reader,
2025-03-14T04:33:15.2960796Z     >>> )
2025-03-14T04:33:15.2960935Z 
2025-03-14T04:33:15.2961143Z     >>> # module.load_state_dict() function might have customized steps
2025-03-14T04:33:15.2961601Z     >>> # to flush the state_dict, must call it to
2025-03-14T04:33:15.2961962Z     >>> # ensure correct behavior.
2025-03-14T04:33:15.2962379Z     >>> my_model.load_state_dict(model_state_dict)
2025-03-14T04:33:15.2962722Z 
2025-03-14T04:33:15.2962822Z .. note::
2025-03-14T04:33:15.2963172Z     load_state_dict uses collectives to coordinate reads across ranks.
2025-03-14T04:33:15.2963729Z     For NCCL-based process groups, internal tensor representations of
2025-03-14T04:33:15.2964299Z     objects must be moved to the GPU device before communication takes place.
2025-03-14T04:33:15.2964878Z     In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-14T04:33:15.2965454Z     and it is the user's responsibility to ensure that this is set so that each
2025-03-14T04:33:15.2965996Z     rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-14T04:33:15.2966396Z 
2025-03-14T04:33:15.2966738Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2967111Z 
2025-03-14T04:33:15.2967769Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=85.
2025-03-14T04:33:15.2968741Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2969135Z 
2025-03-14T04:33:15.2969264Z Save a distributed model in SPMD style.
2025-03-14T04:33:15.2969509Z 
2025-03-14T04:33:15.2969707Z This function is different from ``torch.save()`` as it handles
2025-03-14T04:33:15.2970285Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2025-03-14T04:33:15.2970676Z 
2025-03-14T04:33:15.2970939Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-14T04:33:15.2971484Z save will call ``state_dict`` before serialization.
2025-03-14T04:33:15.2971761Z 
2025-03-14T04:33:15.2971864Z .. warning::
2025-03-14T04:33:15.2972252Z     There is no guarantees of Backwards Compatibility across PyTorch versions
2025-03-14T04:33:15.2972755Z     for saved state_dicts.
2025-03-14T04:33:15.2972949Z 
2025-03-14T04:33:15.2973046Z .. warning::
2025-03-14T04:33:15.2973405Z     If using the `process_group` argument, make sure that only its ranks
2025-03-14T04:33:15.2973950Z     call `save_state_dict` and that all data in state_dict belong to it.
2025-03-14T04:33:15.2974294Z 
2025-03-14T04:33:15.2974390Z .. note::
2025-03-14T04:33:15.2974795Z     When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2025-03-14T04:33:15.2975443Z     the shard_group should be calling `save_state_dict` and the corresponding process
2025-03-14T04:33:15.2975947Z     group needs to be passed in.
2025-03-14T04:33:15.2976150Z 
2025-03-14T04:33:15.2976261Z .. note::
2025-03-14T04:33:15.2976668Z     If no process group is available, this function assumes the intention is to save the
2025-03-14T04:33:15.2977184Z      state_dict in the local process.
2025-03-14T04:33:15.2977399Z 
2025-03-14T04:33:15.2977505Z .. note:
2025-03-14T04:33:15.2977783Z     Rank 0 is assumed to be the coordinator rank.
2025-03-14T04:33:15.2978031Z 
2025-03-14T04:33:15.2978035Z 
2025-03-14T04:33:15.2978140Z Args:
2025-03-14T04:33:15.2978432Z     state_dict (Dict[str, Any]): The state_dict to save.
2025-03-14T04:33:15.2978855Z     checkpoint_id (Union[str, os.PathLike, None]):
2025-03-14T04:33:15.2979366Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-14T04:33:15.2979912Z         depends on the storage. It can be a path to a folder or to a file.
2025-03-14T04:33:15.2980417Z         It can also be a key if the storage is a key-value store.
2025-03-14T04:33:15.2980800Z         (Default: ``None``)
2025-03-14T04:33:15.2981123Z     storage_writer (Optional[StorageWriter]):
2025-03-14T04:33:15.2981589Z         Instance of StorageWriter used to perform writes. If this is not
2025-03-14T04:33:15.2982136Z         specified, DCP will automatically infer the writer based on the
2025-03-14T04:33:15.2982671Z         checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-14T04:33:15.2983113Z         be raised. (Default: ``None``)
2025-03-14T04:33:15.2983456Z     planner (Optional[SavePlanner]):
2025-03-14T04:33:15.2983885Z         Instance of SavePlanner. If this is not specificed, the default
2025-03-14T04:33:15.2984345Z         planner will be used. (Default: ``None``)
2025-03-14T04:33:15.2984726Z     process_group (Optional[ProcessGroup]):
2025-03-14T04:33:15.2985162Z         ProcessGroup to be used for cross-rank synchronization.
2025-03-14T04:33:15.2985571Z         (Default: ``None``)
2025-03-14T04:33:15.2985857Z     no_dist (bool):
2025-03-14T04:33:15.2986193Z         If ``True``, this function will assume the intent is to load
2025-03-14T04:33:15.2986668Z         a checkpoint without using cross-rank synchronization.
2025-03-14T04:33:15.2987071Z         (Default: ``False``)
2025-03-14T04:33:15.2987319Z 
2025-03-14T04:33:15.2987430Z Returns:
2025-03-14T04:33:15.2987733Z     Metadata: Metadata object for the saved checkpoint.
2025-03-14T04:33:15.2988006Z 
2025-03-14T04:33:15.2988118Z Example:
2025-03-14T04:33:15.2988362Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.2988639Z     >>> my_model = MyModule()
2025-03-14T04:33:15.2988843Z 
2025-03-14T04:33:15.2988958Z     >>> state_dict = {"model": my_model}
2025-03-14T04:33:15.2989194Z 
2025-03-14T04:33:15.2989433Z     >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-14T04:33:15.2989899Z     ...     "/checkpoint/1"
2025-03-14T04:33:15.2990182Z     ... )
2025-03-14T04:33:15.2990453Z     >>> torch.distributed.checkpoint.save(
2025-03-14T04:33:15.2990819Z     >>>     state_dict=state_dict,
2025-03-14T04:33:15.2991156Z     >>>     storage_writer=fs_storage_writer,
2025-03-14T04:33:15.2991489Z     >>> )
2025-03-14T04:33:15.2991630Z 
2025-03-14T04:33:15.2991723Z .. note::
2025-03-14T04:33:15.2992086Z     save_state_dict uses collectives to coordinate writes across ranks.
2025-03-14T04:33:15.2992648Z     For NCCL-based process groups, internal tensor representations of
2025-03-14T04:33:15.2993253Z     objects must be moved to the GPU device before communication takes place.
2025-03-14T04:33:15.2993842Z     In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-14T04:33:15.2994407Z     and it is the user's responsibility to ensure that this is set so that
2025-03-14T04:33:15.2994954Z     each rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-14T04:33:15.2995270Z 
2025-03-14T04:33:15.2995543Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.2995911Z 
2025-03-14T04:33:15.2996552Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=195.
2025-03-14T04:33:15.2997559Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.2998219Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2025-03-14T04:33:15.2998912Z     staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2025-03-14T04:33:15.2999406Z 
2025-03-14T04:33:15.2999527Z     .. warning::
2025-03-14T04:33:15.2999855Z         This feature is experimental and subject to change.
2025-03-14T04:33:15.3000130Z 
2025-03-14T04:33:15.3000241Z     Args:
2025-03-14T04:33:15.3000585Z         state_dict (Dict[str, Any]): The state_dict to save.
2025-03-14T04:33:15.3001025Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-03-14T04:33:15.3001515Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-14T04:33:15.3002064Z             depends on the storage. It can be a path to a folder or to a file.
2025-03-14T04:33:15.3002572Z             It can also be a key if the storage is a key-value store.
2025-03-14T04:33:15.3002960Z             (Default: ``None``)
2025-03-14T04:33:15.3003297Z         storage_writer (Optional[StorageWriter]):
2025-03-14T04:33:15.3003767Z             Instance of StorageWriter used to perform 'stage' and  'save'. If
2025-03-14T04:33:15.3004351Z             this is not specified, DCP will automatically infer the writer based on the
2025-03-14T04:33:15.3004925Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-14T04:33:15.3005375Z             be raised. (Default: ``None``)
2025-03-14T04:33:15.3005728Z         planner (Optional[SavePlanner]):
2025-03-14T04:33:15.3006177Z             Instance of SavePlanner. If this is not specificed, the default
2025-03-14T04:33:15.3006648Z             planner will be used. (Default: ``None``)
2025-03-14T04:33:15.3007037Z         process_group (Optional[ProcessGroup]):
2025-03-14T04:33:15.3007480Z             ProcessGroup to be used for cross-rank synchronization.
2025-03-14T04:33:15.3007896Z             (Default: ``None``)
2025-03-14T04:33:15.3008118Z 
2025-03-14T04:33:15.3008254Z     Returns:
2025-03-14T04:33:15.3008619Z         Future: A future holding the resultant Metadata object from `save`.
2025-03-14T04:33:15.3008953Z 
2025-03-14T04:33:15.3009064Z     Example:
2025-03-14T04:33:15.3009316Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:15.3009623Z         >>> my_model = MyModule()
2025-03-14T04:33:15.3009821Z 
2025-03-14T04:33:15.3009956Z         >>> state_dict = {"model": my_model}
2025-03-14T04:33:15.3010180Z 
2025-03-14T04:33:15.3010438Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-14T04:33:15.3010904Z         ...     "/checkpoint/1"
2025-03-14T04:33:15.3011193Z         ... )
2025-03-14T04:33:15.3011553Z         >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2025-03-14T04:33:15.3012006Z         >>>     state_dict=state_dict,
2025-03-14T04:33:15.3012340Z         >>>     storage_writer=fs_storage_writer,
2025-03-14T04:33:15.3012679Z         >>> )
2025-03-14T04:33:15.3012915Z         >>>
2025-03-14T04:33:15.3013161Z         >>> # ... do some work ...
2025-03-14T04:33:15.3013458Z         >>>
2025-03-14T04:33:15.3013712Z         >>> checkpoint_future.result()
2025-03-14T04:33:15.3013972Z 
2025-03-14T04:33:15.3014060Z     
2025-03-14T04:33:15.3014439Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.3014820Z 
2025-03-14T04:33:15.3053062Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=94.
2025-03-14T04:33:15.3054140Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.3054529Z 
2025-03-14T04:33:15.3054734Z Initialize rendezvous event object and record its operations.
2025-03-14T04:33:15.3055061Z 
2025-03-14T04:33:15.3055150Z Args:
2025-03-14T04:33:15.3055414Z     run_id (str): The run id of the rendezvous.
2025-03-14T04:33:15.3055823Z     message (str): The message describing the event.
2025-03-14T04:33:15.3056362Z     node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2025-03-14T04:33:15.3056943Z     name (str): Event name. (E.g. Current action being performed).
2025-03-14T04:33:15.3057371Z     hostname (str): Hostname of the node.
2025-03-14T04:33:15.3057759Z     pid (Optional[int]): The process id of the node.
2025-03-14T04:33:15.3058270Z     master_endpoint (str): The master endpoint for the rendezvous store, if known.
2025-03-14T04:33:15.3058999Z     local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2025-03-14T04:33:15.3059560Z     rank (Optional[int]): The rank of the node, if known.
2025-03-14T04:33:15.3059937Z Returns:
2025-03-14T04:33:15.3060164Z     None
2025-03-14T04:33:15.3060388Z Example:
2025-03-14T04:33:15.3060655Z     >>> # See DynamicRendezvousHandler class
2025-03-14T04:33:15.3061001Z     >>> def _record(
2025-03-14T04:33:15.3061244Z     ...     self,
2025-03-14T04:33:15.3061500Z     ...     message: str,
2025-03-14T04:33:15.3061834Z     ...     node_state: NodeState = NodeState.RUNNING,
2025-03-14T04:33:15.3062210Z     ...     rank: Optional[int] = None,
2025-03-14T04:33:15.3062541Z     ... ) -> None:
2025-03-14T04:33:15.3062827Z     ...     construct_and_record_rdzv_event(
2025-03-14T04:33:15.3063240Z     ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2025-03-14T04:33:15.3063664Z     ...         run_id=self._settings.run_id,
2025-03-14T04:33:15.3064017Z     ...         message=message,
2025-03-14T04:33:15.3064329Z     ...         node_state=node_state,
2025-03-14T04:33:15.3064676Z     ...         hostname=self._this_node.addr,
2025-03-14T04:33:15.3065032Z     ...         pid=self._this_node.pid,
2025-03-14T04:33:15.3065389Z     ...         local_id=self._this_node.local_id,
2025-03-14T04:33:15.3065737Z     ...         rank=rank,
2025-03-14T04:33:15.3066015Z     ...     )
2025-03-14T04:33:15.3066149Z 
2025-03-14T04:33:15.3066471Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.3066877Z 
2025-03-14T04:33:15.6182359Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114.
2025-03-14T04:33:15.6184271Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6185028Z 
2025-03-14T04:33:15.6185365Z This configures FSDP-native mixed precision training.
2025-03-14T04:33:15.6185929Z 
2025-03-14T04:33:15.6186124Z Attributes:
2025-03-14T04:33:15.6186831Z     param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2025-03-14T04:33:15.6187903Z         parameters during forward and backward and thus the dtype for
2025-03-14T04:33:15.6188931Z         forward and backward computation. Outside forward and backward, the
2025-03-14T04:33:15.6189964Z         *sharded* parameters are kept in full precision (e.g. for the
2025-03-14T04:33:15.6190979Z         optimizer step), and for model checkpointing, the parameters are
2025-03-14T04:33:15.6191913Z         always saved in full precision. (Default: ``None``)
2025-03-14T04:33:15.6192844Z     reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-14T04:33:15.6194169Z         gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2025-03-14T04:33:15.6195150Z         ``None`` but ``param_dtype`` is not ``None``, then this takes on
2025-03-14T04:33:15.6196125Z         the ``param_dtype`` value, still running gradient reduction in low
2025-03-14T04:33:15.6197150Z         precision. This is permitted to differ from ``param_dtype``, e.g.
2025-03-14T04:33:15.6197534Z         to force gradient reduction to run in full precision. (Default:
2025-03-14T04:33:15.6197730Z         ``None``)
2025-03-14T04:33:15.6198144Z     buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-14T04:33:15.6198537Z         buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2025-03-14T04:33:15.6198937Z         ``buffer_dtype`` in the first forward pass and keeps them in that
2025-03-14T04:33:15.6199441Z         dtype thereafter. For model checkpointing, the buffers are saved
2025-03-14T04:33:15.6199803Z         in full precision except for ``LOCAL_STATE_DICT``. (Default:
2025-03-14T04:33:15.6199990Z         ``None``)
2025-03-14T04:33:15.6200358Z     keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2025-03-14T04:33:15.6200793Z         gradients to full precision after the backward pass in preparation
2025-03-14T04:33:15.6201318Z         for the optimizer step. If ``True``, then FSDP keeps the gradients
2025-03-14T04:33:15.6201714Z         in the dtype used for gradient reduction, which can save memory if
2025-03-14T04:33:15.6202118Z         using a custom optimizer that supports running in low precision.
2025-03-14T04:33:15.6202324Z         (Default: ``False``)
2025-03-14T04:33:15.6202722Z     cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2025-03-14T04:33:15.6203140Z         its forward args and kwargs to ``param_dtype``. This is to ensure
2025-03-14T04:33:15.6203537Z         that parameter and input dtypes match for forward computation, as
2025-03-14T04:33:15.6203949Z         required by many ops. This may need to be set to ``True`` when only
2025-03-14T04:33:15.6204421Z         applying mixed precision to some but not all FSDP modules, in which
2025-03-14T04:33:15.6204839Z         case a mixed-precision FSDP submodule needs to recast its inputs.
2025-03-14T04:33:15.6205039Z         (Default: ``False``)
2025-03-14T04:33:15.6205462Z     cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2025-03-14T04:33:15.6205851Z         casts its forward args and kwargs to ``param_dtype``, overriding
2025-03-14T04:33:15.6206229Z         the value of ``cast_forward_inputs``. For non-root FSDP modules,
2025-03-14T04:33:15.6206493Z         this does not do anything. (Default: ``True``)
2025-03-14T04:33:15.6206929Z     _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2025-03-14T04:33:15.6207476Z         module classes to ignore for mixed precision when using an
2025-03-14T04:33:15.6207845Z         ``auto_wrap_policy``: Modules of these classes will have FSDP
2025-03-14T04:33:15.6208253Z         applied to them separately with mixed precision disabled (meaning
2025-03-14T04:33:15.6208655Z         that the final FSDP construction would deviate from the specified
2025-03-14T04:33:15.6209029Z         policy). If ``auto_wrap_policy`` is not specified, then this does
2025-03-14T04:33:15.6209419Z         not do anything. This API is experimental and subject to change.
2025-03-14T04:33:15.6209627Z         (Default: ``(_BatchNorm,)``)
2025-03-14T04:33:15.6209655Z 
2025-03-14T04:33:15.6210014Z .. note:: This API is experimental and subject to change.
2025-03-14T04:33:15.6210026Z 
2025-03-14T04:33:15.6210444Z .. note:: Only floating point tensors are cast to their specified dtypes.
2025-03-14T04:33:15.6210451Z 
2025-03-14T04:33:15.6210816Z .. note:: In ``summon_full_params``, parameters are forced to full
2025-03-14T04:33:15.6211055Z     precision, but buffers are not.
2025-03-14T04:33:15.6211063Z 
2025-03-14T04:33:15.6211438Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2025-03-14T04:33:15.6211943Z     their inputs are in a low precision like ``float16`` or ``bfloat16``.
2025-03-14T04:33:15.6212378Z     Disabling FSDP's mixed precision for those norm modules only means that
2025-03-14T04:33:15.6212816Z     the affine parameters are kept in ``float32``. However, this incurs
2025-03-14T04:33:15.6213268Z     separate all-gathers and reduce-scatters for those norm modules, which
2025-03-14T04:33:15.6213701Z     may be inefficient, so if the workload permits, the user should prefer
2025-03-14T04:33:15.6213990Z     to still apply mixed precision to those modules.
2025-03-14T04:33:15.6213999Z 
2025-03-14T04:33:15.6214395Z .. note:: By default, if the user passes a model with any ``_BatchNorm``
2025-03-14T04:33:15.6214809Z     modules and specifies an ``auto_wrap_policy``, then the batch norm
2025-03-14T04:33:15.6215266Z     modules will have FSDP applied to them separately with mixed precision
2025-03-14T04:33:15.6215597Z     disabled. See the ``_module_classes_to_ignore`` argument.
2025-03-14T04:33:15.6215619Z 
2025-03-14T04:33:15.6216041Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2025-03-14T04:33:15.6216444Z     ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2025-03-14T04:33:15.6216874Z     its ``cast_root_forward_inputs`` takes precedence over its
2025-03-14T04:33:15.6217204Z     ``cast_forward_inputs``. For non-root FSDP instances, their
2025-03-14T04:33:15.6217633Z     ``cast_root_forward_inputs`` values are ignored. The default setting is
2025-03-14T04:33:15.6218062Z     sufficient for the typical case where each FSDP instance has the same
2025-03-14T04:33:15.6218516Z     ``MixedPrecision`` configuration and only needs to cast inputs to the
2025-03-14T04:33:15.6218884Z     ``param_dtype`` at the beginning of the model's forward pass.
2025-03-14T04:33:15.6218897Z 
2025-03-14T04:33:15.6219300Z .. note:: For nested FSDP instances with different ``MixedPrecision``
2025-03-14T04:33:15.6219773Z     configurations, we recommend setting individual ``cast_forward_inputs``
2025-03-14T04:33:15.6220179Z     values to configure casting inputs or not before each instance's
2025-03-14T04:33:15.6220534Z     forward. In such a case, since the casts happen before each FSDP
2025-03-14T04:33:15.6220967Z     instance's forward, a parent FSDP instance should have its non-FSDP
2025-03-14T04:33:15.6221375Z     submodules run before its FSDP submodules to avoid the activation dtype
2025-03-14T04:33:15.6221780Z     being changed due to a different ``MixedPrecision`` configuration.
2025-03-14T04:33:15.6221790Z 
2025-03-14T04:33:15.6221972Z     Example::
2025-03-14T04:33:15.6221982Z 
2025-03-14T04:33:15.6222241Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.6222547Z         >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2025-03-14T04:33:15.6222909Z         >>> model[1] = FSDP(
2025-03-14T04:33:15.6223079Z         >>>     model[1],
2025-03-14T04:33:15.6223663Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2025-03-14T04:33:15.6223842Z         >>> )
2025-03-14T04:33:15.6224047Z         >>> model = FSDP(
2025-03-14T04:33:15.6224218Z         >>>     model,
2025-03-14T04:33:15.6224815Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2025-03-14T04:33:15.6224987Z         >>> )
2025-03-14T04:33:15.6224995Z 
2025-03-14T04:33:15.6225423Z     The above shows a working example. On the other hand, if ``model[1]``
2025-03-14T04:33:15.6225799Z     were replaced with ``model[0]``, meaning that the submodule using
2025-03-14T04:33:15.6226243Z     different ``MixedPrecision`` ran its forward first, then ``model[1]``
2025-03-14T04:33:15.6226669Z     would incorrectly see ``float16`` activations instead of ``bfloat16``
2025-03-14T04:33:15.6226858Z     ones.
2025-03-14T04:33:15.6226868Z 
2025-03-14T04:33:15.6226876Z 
2025-03-14T04:33:15.6227359Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6227450Z 
2025-03-14T04:33:15.6228579Z msg = Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295.
2025-03-14T04:33:15.6229078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6229086Z 
2025-03-14T04:33:15.6229486Z ``FullStateDictConfig`` is a config class meant to be used with
2025-03-14T04:33:15.6229861Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
2025-03-14T04:33:15.6230256Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
2025-03-14T04:33:15.6230675Z dicts to save GPU memory and CPU memory, respectively. This config class
2025-03-14T04:33:15.6231055Z is meant to be used via the :func:`state_dict_type` context manager as
2025-03-14T04:33:15.6231227Z follows:
2025-03-14T04:33:15.6231251Z 
2025-03-14T04:33:15.6231492Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.6231933Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:15.6232190Z     >>> fsdp = FSDP(model, auto_wrap_policy=...)
2025-03-14T04:33:15.6232580Z     >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
2025-03-14T04:33:15.6233082Z     >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
2025-03-14T04:33:15.6233302Z     >>>     state = fsdp.state_dict()
2025-03-14T04:33:15.6233694Z     >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
2025-03-14T04:33:15.6234137Z     >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
2025-03-14T04:33:15.6234593Z     >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
2025-03-14T04:33:15.6234786Z     >>> if dist.get_rank() == 0:
2025-03-14T04:33:15.6235141Z     >>> # Load checkpoint only on rank 0 to avoid memory redundancy
2025-03-14T04:33:15.6235406Z     >>>     state_dict = torch.load("my_checkpoint.pt")
2025-03-14T04:33:15.6235658Z     >>>     model.load_state_dict(state_dict)
2025-03-14T04:33:15.6236080Z     >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
2025-03-14T04:33:15.6236551Z     >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
2025-03-14T04:33:15.6236722Z     >>> fsdp = FSDP(
2025-03-14T04:33:15.6236904Z     ...     model,
2025-03-14T04:33:15.6237147Z     ...     device_id=torch.cuda.current_device(),
2025-03-14T04:33:15.6237365Z     ...     auto_wrap_policy=...,
2025-03-14T04:33:15.6237559Z     ...     sync_module_states=True,
2025-03-14T04:33:15.6237732Z     ... )
2025-03-14T04:33:15.6238132Z     >>> # After this point, all ranks have FSDP model with loaded checkpoint.
2025-03-14T04:33:15.6238142Z 
2025-03-14T04:33:15.6238334Z Attributes:
2025-03-14T04:33:15.6238767Z     rank0_only (bool): If ``True``, then only rank 0 saves the full state
2025-03-14T04:33:15.6239316Z         dict, and nonzero ranks save an empty dict. If ``False``, then all
2025-03-14T04:33:15.6239621Z         ranks save the full state dict. (Default: ``False``)
2025-03-14T04:33:15.6239631Z 
2025-03-14T04:33:15.6240109Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6240119Z 
2025-03-14T04:33:15.6310290Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=639.
2025-03-14T04:33:15.6310600Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6310878Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-14T04:33:15.6310885Z 
2025-03-14T04:33:15.6311154Z         Also takes (optional) configuration for the model's and optimizer's state dict.
2025-03-14T04:33:15.6311405Z         The target module does not have to be a FSDP module. If the target
2025-03-14T04:33:15.6311621Z         module is a FSDP module, its ``state_dict_type`` will also be changed.
2025-03-14T04:33:15.6311789Z 
2025-03-14T04:33:15.6312026Z         .. note:: This API should be called for only the top-level (root)
2025-03-14T04:33:15.6312124Z             module.
2025-03-14T04:33:15.6312129Z 
2025-03-14T04:33:15.6312364Z         .. note:: This API enables users to transparently use the conventional
2025-03-14T04:33:15.6312561Z             ``state_dict`` API to take model checkpoints in cases where the
2025-03-14T04:33:15.6312789Z             root FSDP module is wrapped by another ``nn.Module``. For example,
2025-03-14T04:33:15.6313005Z             the following will ensure ``state_dict`` is called on all non-FSDP
2025-03-14T04:33:15.6313260Z             instances, while dispatching into `sharded_state_dict` implementation
2025-03-14T04:33:15.6313362Z             for FSDP:
2025-03-14T04:33:15.6313372Z 
2025-03-14T04:33:15.6313488Z         Example::
2025-03-14T04:33:15.6313493Z 
2025-03-14T04:33:15.6313634Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.6313768Z             >>> model = DDP(FSDP(...))
2025-03-14T04:33:15.6313887Z             >>> FSDP.set_state_dict_type(
2025-03-14T04:33:15.6314006Z             >>>     model,
2025-03-14T04:33:15.6314146Z             >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-14T04:33:15.6314433Z             >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2025-03-14T04:33:15.6314669Z             >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2025-03-14T04:33:15.6314779Z             >>> )
2025-03-14T04:33:15.6314919Z             >>> param_state_dict = model.state_dict()
2025-03-14T04:33:15.6315117Z             >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-14T04:33:15.6315122Z 
2025-03-14T04:33:15.6315218Z         Args:
2025-03-14T04:33:15.6315374Z             module (torch.nn.Module): Root module.
2025-03-14T04:33:15.6315612Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-14T04:33:15.6315880Z             state_dict_config (Optional[StateDictConfig]): the configuration for the
2025-03-14T04:33:15.6316001Z                 target ``state_dict_type``.
2025-03-14T04:33:15.6316273Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2025-03-14T04:33:15.6316401Z                 for the optimizer state dict.
2025-03-14T04:33:15.6316406Z 
2025-03-14T04:33:15.6316521Z         Returns:
2025-03-14T04:33:15.6316755Z             A StateDictSettings that include the previous state_dict type and
2025-03-14T04:33:15.6316897Z             configuration for the module.
2025-03-14T04:33:15.6316991Z         
2025-03-14T04:33:15.6317267Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6317272Z 
2025-03-14T04:33:15.6318141Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=797.
2025-03-14T04:33:15.6318448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6318721Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-14T04:33:15.6318725Z 
2025-03-14T04:33:15.6319051Z         This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2025-03-14T04:33:15.6319279Z         :meth:`set_state_dict_type` for the detail.
2025-03-14T04:33:15.6319285Z 
2025-03-14T04:33:15.6319387Z         Example::
2025-03-14T04:33:15.6319391Z 
2025-03-14T04:33:15.6319550Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.6319672Z             >>> model = DDP(FSDP(...))
2025-03-14T04:33:15.6319814Z             >>> with FSDP.state_dict_type(
2025-03-14T04:33:15.6319916Z             >>>     model,
2025-03-14T04:33:15.6320077Z             >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-14T04:33:15.6320174Z             >>> ):
2025-03-14T04:33:15.6320361Z             >>>     checkpoint = model.state_dict()
2025-03-14T04:33:15.6320366Z 
2025-03-14T04:33:15.6320461Z         Args:
2025-03-14T04:33:15.6320611Z             module (torch.nn.Module): Root module.
2025-03-14T04:33:15.6320853Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-14T04:33:15.6321108Z             state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2025-03-14T04:33:15.6321279Z                 configuration for the target ``state_dict_type``.
2025-03-14T04:33:15.6321539Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2025-03-14T04:33:15.6321741Z                ``state_dict`` configuration for the target ``state_dict_type``.
2025-03-14T04:33:15.6321849Z         
2025-03-14T04:33:15.6322108Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6322114Z 
2025-03-14T04:33:15.6366405Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1810.
2025-03-14T04:33:15.6366680Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6366685Z 
2025-03-14T04:33:15.6367014Z Transform the state-dict of an optimizer corresponding to a sharded model.
2025-03-14T04:33:15.6367019Z 
2025-03-14T04:33:15.6367219Z The given state-dict can be transformed to one of three types:
2025-03-14T04:33:15.6367541Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2025-03-14T04:33:15.6367546Z 
2025-03-14T04:33:15.6367799Z For full optimizer state_dict, all states are unflattened and not sharded.
2025-03-14T04:33:15.6368017Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2025-03-14T04:33:15.6368117Z avoid OOM.
2025-03-14T04:33:15.6368139Z 
2025-03-14T04:33:15.6368378Z For sharded optimizer state_dict, all states are unflattened but sharded.
2025-03-14T04:33:15.6368606Z CPU only can be specified via :meth:`state_dict_type` to further save
2025-03-14T04:33:15.6368704Z memory.
2025-03-14T04:33:15.6368709Z 
2025-03-14T04:33:15.6368945Z For local state_dict, no transformation will be performed. But a state
2025-03-14T04:33:15.6369188Z will be converted from nn.Tensor to ShardedTensor to represent its sharding
2025-03-14T04:33:15.6369327Z nature (this is not supported yet).
2025-03-14T04:33:15.6369332Z 
2025-03-14T04:33:15.6369435Z Example::
2025-03-14T04:33:15.6369439Z 
2025-03-14T04:33:15.6369605Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.6369846Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:15.6370023Z     >>> from torch.distributed.fsdp import StateDictType
2025-03-14T04:33:15.6370249Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-14T04:33:15.6370504Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-14T04:33:15.6370619Z     >>> # Save a checkpoint
2025-03-14T04:33:15.6370743Z     >>> model, optim = ...
2025-03-14T04:33:15.6370857Z     >>> FSDP.set_state_dict_type(
2025-03-14T04:33:15.6370966Z     >>>     model,
2025-03-14T04:33:15.6371093Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:15.6371254Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6371407Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6371516Z     >>> )
2025-03-14T04:33:15.6371634Z     >>> state_dict = model.state_dict()
2025-03-14T04:33:15.6371822Z     >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-14T04:33:15.6371976Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-14T04:33:15.6372097Z     >>> # Load a checkpoint
2025-03-14T04:33:15.6372203Z     >>> model, optim = ...
2025-03-14T04:33:15.6372382Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-14T04:33:15.6372497Z     >>> FSDP.set_state_dict_type(
2025-03-14T04:33:15.6372607Z     >>>     model,
2025-03-14T04:33:15.6372773Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:15.6372910Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6373076Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6373168Z     >>> )
2025-03-14T04:33:15.6373306Z     >>> model.load_state_dict(state_dict)
2025-03-14T04:33:15.6373466Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-14T04:33:15.6373602Z     >>>     model, optim, optim_state_dict
2025-03-14T04:33:15.6373695Z     >>> )
2025-03-14T04:33:15.6373843Z     >>> optim.load_state_dict(optim_state_dict)
2025-03-14T04:33:15.6373847Z 
2025-03-14T04:33:15.6373939Z Args:
2025-03-14T04:33:15.6374156Z     model (torch.nn.Module): Root module (which may or may not be a
2025-03-14T04:33:15.6374369Z         :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-14T04:33:15.6374525Z         were passed into the optimizer ``optim``.
2025-03-14T04:33:15.6374715Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-14T04:33:15.6374832Z         parameters.
2025-03-14T04:33:15.6375051Z     optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2025-03-14T04:33:15.6375285Z         transform. If the value is None, optim.state_dict() will be used. (
2025-03-14T04:33:15.6375425Z         Default: ``None``)
2025-03-14T04:33:15.6375684Z     group (dist.ProcessGroup): Model's process group across which parameters
2025-03-14T04:33:15.6375875Z         are sharded or ``None`` if using the default process group. (
2025-03-14T04:33:15.6375995Z         Default: ``None``)
2025-03-14T04:33:15.6376000Z 
2025-03-14T04:33:15.6376097Z Returns:
2025-03-14T04:33:15.6376308Z     Dict[str, Any]: A :class:`dict` containing the optimizer state for
2025-03-14T04:33:15.6376482Z     ``model``. The sharding of the optimizer state is based on
2025-03-14T04:33:15.6376605Z     ``state_dict_type``.
2025-03-14T04:33:15.6376609Z 
2025-03-14T04:33:15.6376869Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6376875Z 
2025-03-14T04:33:15.6377755Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1908.
2025-03-14T04:33:15.6378040Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6378045Z 
2025-03-14T04:33:15.6378404Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2025-03-14T04:33:15.6378408Z 
2025-03-14T04:33:15.6378595Z Given a ``optim_state_dict`` that is transformed through
2025-03-14T04:33:15.6378815Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2025-03-14T04:33:15.6379083Z state_dict that can be loaded to ``optim`` which is the optimizer for
2025-03-14T04:33:15.6379311Z ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2025-03-14T04:33:15.6379318Z 
2025-03-14T04:33:15.6379470Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.6379714Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:15.6379895Z     >>> from torch.distributed.fsdp import StateDictType
2025-03-14T04:33:15.6380083Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-14T04:33:15.6380310Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-14T04:33:15.6380419Z     >>> # Save a checkpoint
2025-03-14T04:33:15.6380543Z     >>> model, optim = ...
2025-03-14T04:33:15.6380661Z     >>> FSDP.set_state_dict_type(
2025-03-14T04:33:15.6380772Z     >>>     model,
2025-03-14T04:33:15.6380905Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:15.6381059Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6381220Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6381329Z     >>> )
2025-03-14T04:33:15.6381452Z     >>> state_dict = model.state_dict()
2025-03-14T04:33:15.6381620Z     >>> original_osd = optim.state_dict()
2025-03-14T04:33:15.6381759Z     >>> optim_state_dict = FSDP.optim_state_dict(
2025-03-14T04:33:15.6381871Z     >>>     model,
2025-03-14T04:33:15.6381970Z     >>>     optim,
2025-03-14T04:33:15.6382101Z     >>>     optim_state_dict=original_osd
2025-03-14T04:33:15.6382193Z     >>> )
2025-03-14T04:33:15.6382360Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-14T04:33:15.6382465Z     >>> # Load a checkpoint
2025-03-14T04:33:15.6382586Z     >>> model, optim = ...
2025-03-14T04:33:15.6382744Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-14T04:33:15.6382874Z     >>> FSDP.set_state_dict_type(
2025-03-14T04:33:15.6382971Z     >>>     model,
2025-03-14T04:33:15.6383112Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:15.6383251Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6383415Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:15.6383512Z     >>> )
2025-03-14T04:33:15.6383635Z     >>> model.load_state_dict(state_dict)
2025-03-14T04:33:15.6383806Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-14T04:33:15.6383926Z     >>>     model, optim, optim_state_dict
2025-03-14T04:33:15.6384031Z     >>> )
2025-03-14T04:33:15.6384189Z     >>> optim.load_state_dict(optim_state_dict)
2025-03-14T04:33:15.6384194Z 
2025-03-14T04:33:15.6384299Z Args:
2025-03-14T04:33:15.6384506Z     model (torch.nn.Module): Root module (which may or may not be a
2025-03-14T04:33:15.6384731Z         :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-14T04:33:15.6384872Z         were passed into the optimizer ``optim``.
2025-03-14T04:33:15.6385078Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-14T04:33:15.6385183Z         parameters.
2025-03-14T04:33:15.6385429Z     optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2025-03-14T04:33:15.6385636Z     is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2025-03-14T04:33:15.6385852Z         KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2025-03-14T04:33:15.6386036Z         KeyedOptimizer or torch.distributed's NamedOptimizer.
2025-03-14T04:33:15.6386253Z     load_directly (bool): If this is set to True, this API will also
2025-03-14T04:33:15.6386462Z         call optim.load_state_dict(result) before returning the result.
2025-03-14T04:33:15.6386704Z         Otherwise, users are responsible to call ``optim.load_state_dict()``
2025-03-14T04:33:15.6386812Z         (Default: ``False``)
2025-03-14T04:33:15.6387069Z     group (dist.ProcessGroup): Model's process group across which parameters
2025-03-14T04:33:15.6387261Z         are sharded or ``None`` if using the default process group. (
2025-03-14T04:33:15.6387381Z         Default: ``None``)
2025-03-14T04:33:15.6387386Z 
2025-03-14T04:33:15.6387719Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6387724Z 
2025-03-14T04:33:15.6811724Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=128.
2025-03-14T04:33:15.6812033Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6812039Z 
2025-03-14T04:33:15.6812274Z RemoteModule instance can only be created after RPC initialization.
2025-03-14T04:33:15.6812278Z 
2025-03-14T04:33:15.6812517Z It creates a user-specified module on a specified remote node.
2025-03-14T04:33:15.6812756Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-14T04:33:15.6812886Z executed on the remote node.
2025-03-14T04:33:15.6813124Z It takes care of autograd recording to ensure the backward pass propagates
2025-03-14T04:33:15.6813305Z gradients back to the corresponding remote module.
2025-03-14T04:33:15.6813675Z It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2025-03-14T04:33:15.6814034Z without incurring any overheads of copying the actual module,
2025-03-14T04:33:15.6814241Z which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2025-03-14T04:33:15.6814379Z pointing to the remote module.
2025-03-14T04:33:15.6814383Z 
2025-03-14T04:33:15.6814616Z The arguments of ``forward_async`` and ``forward`` are the same as
2025-03-14T04:33:15.6814853Z the ``forward`` method of the module returned by the ``module_cls``.
2025-03-14T04:33:15.6814857Z 
2025-03-14T04:33:15.6815188Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2025-03-14T04:33:15.6815192Z 
2025-03-14T04:33:15.6815447Z Particularly, to create a hybrid model, typically the local modules should be
2025-03-14T04:33:15.6815839Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2025-03-14T04:33:15.6815947Z Hybrid Example:
2025-03-14T04:33:15.6816092Z         >>> class HybridModel(nn.Module):
2025-03-14T04:33:15.6816218Z         >>>     def __init__(self) -> None:
2025-03-14T04:33:15.6816357Z         >>>         nn.Module.__init__(self)
2025-03-14T04:33:15.6816510Z         >>>         self.remote_embedding = RemoteModule(...)
2025-03-14T04:33:15.6816661Z         >>>         self.local_linear = nn.Linear(...)
2025-03-14T04:33:15.6816665Z 
2025-03-14T04:33:15.6816921Z For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-14T04:33:15.6817195Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2025-03-14T04:33:15.6817411Z the generated ``RemoteModule`` will have 2 methods in signature of
2025-03-14T04:33:15.6817566Z ``def forward(input: Tensor) -> Tensor:`` and
2025-03-14T04:33:15.6817740Z ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2025-03-14T04:33:15.6817744Z 
2025-03-14T04:33:15.6817878Z .. note::
2025-03-14T04:33:15.6818041Z     If the remote module is placed on a cuda device,
2025-03-14T04:33:15.6818300Z     any input CPU tensors will be automatically moved to the same cuda device,
2025-03-14T04:33:15.6818701Z     and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2025-03-14T04:33:15.6818706Z 
2025-03-14T04:33:15.6818815Z Args:
2025-03-14T04:33:15.6819119Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-14T04:33:15.6819435Z         The device can be a local device or a remote device specified by one of the following remote
2025-03-14T04:33:15.6819534Z         formats:
2025-03-14T04:33:15.6819538Z 
2025-03-14T04:33:15.6819701Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-14T04:33:15.6819860Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-14T04:33:15.6819864Z 
2025-03-14T04:33:15.6820174Z         In addition, the device field can be optional and the default value is "cpu".
2025-03-14T04:33:15.6820340Z     module_cls (nn.Module): For example,
2025-03-14T04:33:15.6820482Z         >>> class MyModule(nn.Module):
2025-03-14T04:33:15.6820600Z         >>>     def forward(input):
2025-03-14T04:33:15.6820729Z         >>>         return input + 1
2025-03-14T04:33:15.6820825Z         >>>
2025-03-14T04:33:15.6820952Z         >>> module_cls = MyModule
2025-03-14T04:33:15.6821154Z     args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-14T04:33:15.6821368Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-14T04:33:15.6821649Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-14T04:33:15.6821903Z         to be created. The type object should be decorated by @torch.jit.interface.
2025-03-14T04:33:15.6822130Z         If not provided, the generated RemoteModule is not torchscript-able.
2025-03-14T04:33:15.6822385Z         Warning, this is an experimental API and susceptible to frequent changes.
2025-03-14T04:33:15.6822392Z 
2025-03-14T04:33:15.6822488Z Returns:
2025-03-14T04:33:15.6822749Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-14T04:33:15.6823473Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-14T04:33:15.6823768Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-14T04:33:15.6823920Z     on the user-provided module on the remote side.
2025-03-14T04:33:15.6823925Z 
2025-03-14T04:33:15.6824041Z Example::
2025-03-14T04:33:15.6824203Z     Run the following code in two different processes:
2025-03-14T04:33:15.6824208Z 
2025-03-14T04:33:15.6824345Z     >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:15.6824448Z     >>> # On worker 0:
2025-03-14T04:33:15.6824562Z     >>> import torch
2025-03-14T04:33:15.6824695Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.6824827Z     >>> from torch import nn, Tensor
2025-03-14T04:33:15.6825059Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-14T04:33:15.6825165Z     >>>
2025-03-14T04:33:15.6825314Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:15.6825457Z     >>> remote_linear_module = RemoteModule(
2025-03-14T04:33:15.6825592Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-14T04:33:15.6825702Z     >>> )
2025-03-14T04:33:15.6825816Z     >>> input = torch.randn(128, 20)
2025-03-14T04:33:15.6826023Z     >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-14T04:33:15.6826133Z     >>> ret = ret_fut.wait()
2025-03-14T04:33:15.6826240Z     >>> rpc.shutdown()
2025-03-14T04:33:15.6826260Z 
2025-03-14T04:33:15.6826358Z     >>> # On worker 1:
2025-03-14T04:33:15.6826456Z     >>> import torch
2025-03-14T04:33:15.6826602Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.6826699Z     >>>
2025-03-14T04:33:15.6826856Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:15.6826957Z     >>> rpc.shutdown()
2025-03-14T04:33:15.6826965Z 
2025-03-14T04:33:15.6827237Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6827245Z 
2025-03-14T04:33:15.6828094Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=505.
2025-03-14T04:33:15.6828366Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6828374Z 
2025-03-14T04:33:15.6828717Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2025-03-14T04:33:15.6828722Z 
2025-03-14T04:33:15.6829048Z This alternate initialization method can be particularly useful if we want to create multiple
2025-03-14T04:33:15.6829390Z RemoteModule instances that share the same underlying module and reduce memory consumption.
2025-03-14T04:33:15.6829395Z 
2025-03-14T04:33:15.6829723Z Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2025-03-14T04:33:15.6829936Z which is not supported. The recommended way is as follows:
2025-03-14T04:33:15.6829943Z 
2025-03-14T04:33:15.6830084Z     1. the sender creates a RemoteModule;
2025-03-14T04:33:15.6830236Z     2. the sender sends its ``module_rref`` over RPC;
2025-03-14T04:33:15.6830592Z     3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2025-03-14T04:33:15.6830596Z 
2025-03-14T04:33:15.6830703Z Example::
2025-03-14T04:33:15.6830876Z     Run the following code in two different processes:
2025-03-14T04:33:15.6830880Z 
2025-03-14T04:33:15.6831004Z     >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:15.6831122Z     >>> # On worker 0:
2025-03-14T04:33:15.6831225Z     >>> import torch
2025-03-14T04:33:15.6831380Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.6831500Z     >>> from torch import nn, Tensor
2025-03-14T04:33:15.6831747Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-14T04:33:15.6831846Z     >>>
2025-03-14T04:33:15.6831996Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:15.6832166Z     >>> remote_module = RemoteModule(
2025-03-14T04:33:15.6832304Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-14T04:33:15.6832414Z     >>> )
2025-03-14T04:33:15.6832509Z     >>>
2025-03-14T04:33:15.6832645Z     >>> remote_module1 = rpc.rpc_sync(
2025-03-14T04:33:15.6832754Z     >>>     "worker1/cpu",
2025-03-14T04:33:15.6832909Z     >>>     RemoteModule.init_from_module_rref,
2025-03-14T04:33:15.6833074Z     >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2025-03-14T04:33:15.6833182Z     >>> )
2025-03-14T04:33:15.6833289Z     >>> rpc.shutdown()
2025-03-14T04:33:15.6833293Z 
2025-03-14T04:33:15.6833409Z     >>> # On worker 1:
2025-03-14T04:33:15.6833513Z     >>> import torch
2025-03-14T04:33:15.6833662Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.6833756Z     >>>
2025-03-14T04:33:15.6833924Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:15.6834028Z     >>> rpc.shutdown()
2025-03-14T04:33:15.6834032Z 
2025-03-14T04:33:15.6834145Z Args:
2025-03-14T04:33:15.6834448Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-14T04:33:15.6834758Z         The device can be a local device or a remote device specified by one of the following remote
2025-03-14T04:33:15.6834860Z         formats:
2025-03-14T04:33:15.6834865Z 
2025-03-14T04:33:15.6835053Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-14T04:33:15.6835209Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-14T04:33:15.6835214Z 
2025-03-14T04:33:15.6835478Z         In addition, the device field can be optional and the default value is "cpu".
2025-03-14T04:33:15.6835729Z     module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2025-03-14T04:33:15.6835857Z         the created remote module.
2025-03-14T04:33:15.6836137Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-14T04:33:15.6836383Z         to be created. The type object should be decorated by @torch.jit.interface.
2025-03-14T04:33:15.6836611Z         If not provided, the generated RemoteModule is not torchscript-able.
2025-03-14T04:33:15.6836859Z         Warning, this is an experimental API and susceptible to frequent changes.
2025-03-14T04:33:15.6836864Z 
2025-03-14T04:33:15.6836957Z Returns:
2025-03-14T04:33:15.6837217Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-14T04:33:15.6837452Z     user-provided ``module_rref``, it has a blocking ``forward`` method and an
2025-03-14T04:33:15.6837740Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-14T04:33:15.6837887Z     on the user-provided module on the remote side.
2025-03-14T04:33:15.6837892Z 
2025-03-14T04:33:15.6838188Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6838217Z 
2025-03-14T04:33:15.6838854Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=597.
2025-03-14T04:33:15.6839179Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6839185Z 
2025-03-14T04:33:15.6839442Z     A RemoteModule instance can only be created after RPC initialization.
2025-03-14T04:33:15.6839449Z 
2025-03-14T04:33:15.6839648Z     It creates a user-specified module on a specified remote node.
2025-03-14T04:33:15.6839904Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-14T04:33:15.6840020Z     executed on the remote node.
2025-03-14T04:33:15.6840273Z     It takes care of autograd recording to ensure the backward pass propagates
2025-03-14T04:33:15.6840438Z     gradients back to the corresponding remote module.
2025-03-14T04:33:15.6840442Z 
2025-03-14T04:33:15.6840688Z     It generates two methods ``forward_async`` and ``forward`` based on the
2025-03-14T04:33:15.6840907Z     signature of the ``forward`` method of ``module_cls``. ``forward_async``
2025-03-14T04:33:15.6841209Z     runs asynchronously and returns a Future. The arguments of ``forward_async``
2025-03-14T04:33:15.6841412Z     and ``forward`` are the same as the ``forward`` method of the module
2025-03-14T04:33:15.6841544Z     returned by the ``module_cls``.
2025-03-14T04:33:15.6841549Z 
2025-03-14T04:33:15.6841757Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-14T04:33:15.6842028Z     that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2025-03-14T04:33:15.6842261Z     the generated ``RemoteModule`` will have 2 methods with the signatures:
2025-03-14T04:33:15.6842282Z 
2025-03-14T04:33:15.6842419Z     | ``def forward(input: Tensor) -> Tensor:``
2025-03-14T04:33:15.6842593Z     | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2025-03-14T04:33:15.6842616Z 
2025-03-14T04:33:15.6842711Z Args:
2025-03-14T04:33:15.6843032Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-14T04:33:15.6843379Z         The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2025-03-14T04:33:15.6843543Z         E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2025-03-14T04:33:15.6843842Z         In addition, the device field can be optional and the default value is "cpu".
2025-03-14T04:33:15.6844112Z     module_cls (nn.Module): Class for the module to be created remotely. For example,
2025-03-14T04:33:15.6844116Z 
2025-03-14T04:33:15.6844238Z         >>> class MyModule(nn.Module):
2025-03-14T04:33:15.6844363Z         >>>     def forward(input):
2025-03-14T04:33:15.6844471Z         >>>         return input + 1
2025-03-14T04:33:15.6844582Z         >>>
2025-03-14T04:33:15.6844690Z         >>> module_cls = MyModule
2025-03-14T04:33:15.6844695Z 
2025-03-14T04:33:15.6844922Z     args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-14T04:33:15.6845119Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-14T04:33:15.6845126Z 
2025-03-14T04:33:15.6845238Z Returns:
2025-03-14T04:33:15.6845487Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-14T04:33:15.6845738Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-14T04:33:15.6846015Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-14T04:33:15.6846180Z     on the user-provided module on the remote side.
2025-03-14T04:33:15.6846185Z 
2025-03-14T04:33:15.6846284Z Example::
2025-03-14T04:33:15.6846458Z     Run the following code in two different processes:
2025-03-14T04:33:15.6846462Z 
2025-03-14T04:33:15.6846588Z     >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:15.6846709Z     >>> # On worker 0:
2025-03-14T04:33:15.6846810Z     >>> import torch
2025-03-14T04:33:15.6847023Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.6847142Z     >>> from torch import nn, Tensor
2025-03-14T04:33:15.6847388Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-14T04:33:15.6847485Z     >>>
2025-03-14T04:33:15.6847653Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:15.6847783Z     >>> remote_linear_module = RemoteModule(
2025-03-14T04:33:15.6847918Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-14T04:33:15.6848029Z     >>> )
2025-03-14T04:33:15.6859834Z     >>> input = torch.randn(128, 20)
2025-03-14T04:33:15.6860103Z     >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-14T04:33:15.6860211Z     >>> ret = ret_fut.wait()
2025-03-14T04:33:15.6860325Z     >>> rpc.shutdown()
2025-03-14T04:33:15.6860333Z 
2025-03-14T04:33:15.6860431Z     >>> # On worker 1:
2025-03-14T04:33:15.6860522Z     >>> import torch
2025-03-14T04:33:15.6860658Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.6860784Z     >>>
2025-03-14T04:33:15.6860936Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:15.6861055Z     >>> rpc.shutdown()
2025-03-14T04:33:15.6861183Z 
2025-03-14T04:33:15.6861390Z     Furthermore, a more practical example that is combined with
2025-03-14T04:33:15.6861908Z     `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2025-03-14T04:33:15.6862246Z     can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2025-03-14T04:33:15.6862251Z 
2025-03-14T04:33:15.6862528Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6862534Z 
2025-03-14T04:33:15.6970655Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130.
2025-03-14T04:33:15.6970932Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6970952Z 
2025-03-14T04:33:15.6971215Z DistributedOptimizer takes remote references to parameters scattered
2025-03-14T04:33:15.6971463Z across workers and applies the given optimizer locally for each parameter.
2025-03-14T04:33:15.6971468Z 
2025-03-14T04:33:15.6971729Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2025-03-14T04:33:15.6971889Z to retrieve the gradients for specific parameters.
2025-03-14T04:33:15.6971894Z 
2025-03-14T04:33:15.6972116Z Concurrent calls to
2025-03-14T04:33:15.6972336Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2025-03-14T04:33:15.6972505Z either from the same or different clients, will
2025-03-14T04:33:15.6972736Z be serialized on each worker -- as each worker's optimizer can only work
2025-03-14T04:33:15.6972965Z on one set of gradients at a time. However, there is no guarantee that
2025-03-14T04:33:15.6973221Z the full forward-backward-optimizer sequence will execute for one client
2025-03-14T04:33:15.6973468Z at a time. This means that the gradients being applied may not correspond
2025-03-14T04:33:15.6973699Z to the latest forward pass executed on a given worker. Also, there is no
2025-03-14T04:33:15.6973844Z guaranteed ordering across workers.
2025-03-14T04:33:15.6973849Z 
2025-03-14T04:33:15.6974117Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2025-03-14T04:33:15.6974371Z by default, so that optimizer updates are not blocked by the Python Global
2025-03-14T04:33:15.6974624Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2025-03-14T04:33:15.6974889Z Model Parallel). This feature is currently enabled for most optimizers. You
2025-03-14T04:33:15.6975148Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2025-03-14T04:33:15.6975285Z for your own custom optimizers.
2025-03-14T04:33:15.6975290Z 
2025-03-14T04:33:15.6975383Z Args:
2025-03-14T04:33:15.6975609Z     optimizer_class (optim.Optimizer): the class of optimizer to
2025-03-14T04:33:15.6975822Z         instantiate on each worker.
2025-03-14T04:33:15.6976058Z     params_rref (list[RRef]): list of RRefs to local or remote parameters
2025-03-14T04:33:15.6976165Z         to optimize.
2025-03-14T04:33:15.6976403Z     args: arguments to pass to the optimizer constructor on each worker.
2025-03-14T04:33:15.6976638Z     kwargs: arguments to pass to the optimizer constructor on each worker.
2025-03-14T04:33:15.6976643Z 
2025-03-14T04:33:15.6976772Z Example::
2025-03-14T04:33:15.6976898Z     >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:15.6977089Z     >>> import torch.distributed.autograd as dist_autograd
2025-03-14T04:33:15.6977224Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.6977357Z     >>> from torch import optim
2025-03-14T04:33:15.6977556Z     >>> from torch.distributed.optim import DistributedOptimizer
2025-03-14T04:33:15.6977669Z     >>>
2025-03-14T04:33:15.6977815Z     >>> with dist_autograd.context() as context_id:
2025-03-14T04:33:15.6977948Z     >>>   # Forward pass.
2025-03-14T04:33:15.6978165Z     >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2025-03-14T04:33:15.6978423Z     >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2025-03-14T04:33:15.6978554Z     >>>   loss = rref1.to_here() + rref2.to_here()
2025-03-14T04:33:15.6978662Z     >>>
2025-03-14T04:33:15.6978769Z     >>>   # Backward pass.
2025-03-14T04:33:15.6978943Z     >>>   dist_autograd.backward(context_id, [loss.sum()])
2025-03-14T04:33:15.6979042Z     >>>
2025-03-14T04:33:15.6979160Z     >>>   # Optimizer.
2025-03-14T04:33:15.6979291Z     >>>   dist_optim = DistributedOptimizer(
2025-03-14T04:33:15.6979407Z     >>>      optim.SGD,
2025-03-14T04:33:15.6979511Z     >>>      [rref1, rref2],
2025-03-14T04:33:15.6979629Z     >>>      lr=0.05,
2025-03-14T04:33:15.6979720Z     >>>   )
2025-03-14T04:33:15.6979856Z     >>>   dist_optim.step(context_id)
2025-03-14T04:33:15.6979861Z 
2025-03-14T04:33:15.6980029Z __ https://github.com/pytorch/tutorials/pull/1465
2025-03-14T04:33:15.6980036Z 
2025-03-14T04:33:15.6980313Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6980320Z 
2025-03-14T04:33:15.6982527Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9.
2025-03-14T04:33:15.6982812Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.6982850Z 
2025-03-14T04:33:15.6983241Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-14T04:33:15.6983436Z This optimizer runs local optimizer at every step.
2025-03-14T04:33:15.6983784Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.
2025-03-14T04:33:15.6983788Z 
2025-03-14T04:33:15.6983884Z Args:
2025-03-14T04:33:15.6984017Z     optim: The local optimizer.
2025-03-14T04:33:15.6984245Z     averager: A model averager instance to run post-localSGD algorithm.
2025-03-14T04:33:15.6984249Z 
2025-03-14T04:33:15.6984369Z Example::
2025-03-14T04:33:15.6984375Z 
2025-03-14T04:33:15.6984508Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:15.6984628Z     >>> import torch
2025-03-14T04:33:15.6984754Z     >>> import torch.distributed as dist
2025-03-14T04:33:15.6985049Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-14T04:33:15.6985165Z     >>> import torch.nn as nn
2025-03-14T04:33:15.6985384Z     >>> from torch.distributed.optim import PostLocalSGDOptimizer
2025-03-14T04:33:15.6985659Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-14T04:33:15.6985793Z     >>>   PostLocalSGDState,
2025-03-14T04:33:15.6985903Z     >>>   post_localSGD_hook,
2025-03-14T04:33:15.6986017Z     >>> )
2025-03-14T04:33:15.6986110Z     >>>
2025-03-14T04:33:15.6986274Z     >>> model = nn.parallel.DistributedDataParallel(
2025-03-14T04:33:15.6986521Z     >>>    module, device_ids=[rank], output_device=rank
2025-03-14T04:33:15.6986612Z     >>> )
2025-03-14T04:33:15.6986721Z     >>>
2025-03-14T04:33:15.6986873Z     >>> # Register a post-localSGD communication hook.
2025-03-14T04:33:15.6987188Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-14T04:33:15.6987359Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:15.6987469Z     >>>
2025-03-14T04:33:15.6987684Z     >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2025-03-14T04:33:15.6987950Z     >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2025-03-14T04:33:15.6988125Z     >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-14T04:33:15.6988357Z     >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2025-03-14T04:33:15.6988486Z     >>> opt = PostLocalSGDOptimizer(
2025-03-14T04:33:15.6988619Z     >>>     optim=local_optim,
2025-03-14T04:33:15.6988874Z     >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-14T04:33:15.6989014Z     >>> )
2025-03-14T04:33:15.6989110Z     >>>
2025-03-14T04:33:15.6989359Z     >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2025-03-14T04:33:15.6989665Z     >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2025-03-14T04:33:15.6990061Z     >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2025-03-14T04:33:15.6990177Z     >>> for step in range(0, 200):
2025-03-14T04:33:15.6990301Z     >>>    opt.zero_grad()
2025-03-14T04:33:15.6990424Z     >>>    loss = loss_fn(output, labels)
2025-03-14T04:33:15.6990552Z     >>>    loss.backward()
2025-03-14T04:33:15.6990653Z     >>>    opt.step()
2025-03-14T04:33:15.6990658Z 
2025-03-14T04:33:15.6990932Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.6990941Z 
2025-03-14T04:33:15.7081943Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284.
2025-03-14T04:33:15.7082235Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.7082240Z 
2025-03-14T04:33:15.7082751Z Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2025-03-14T04:33:15.7082756Z 
2025-03-14T04:33:15.7082897Z The sharing is done as described by ZeRO_.
2025-03-14T04:33:15.7082901Z 
2025-03-14T04:33:15.7083078Z The local optimizer instance in each rank is only
2025-03-14T04:33:15.7083335Z responsible for updating approximately ``1 / world_size`` parameters and
2025-03-14T04:33:15.7083569Z hence only needs to keep ``1 / world_size`` optimizer states. After
2025-03-14T04:33:15.7083828Z parameters are updated locally, each rank will broadcast its parameters to
2025-03-14T04:33:15.7084046Z all other peers to keep all model replicas in the same state.
2025-03-14T04:33:15.7084253Z ``ZeroRedundancyOptimizer`` can be used in conjunction with
2025-03-14T04:33:15.7084542Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2025-03-14T04:33:15.7084657Z memory consumption.
2025-03-14T04:33:15.7084661Z 
2025-03-14T04:33:15.7084947Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2025-03-14T04:33:15.7085191Z of parameters at each rank. Each parameter belongs to a single rank and is
2025-03-14T04:33:15.7085477Z not divided among ranks. The partition is arbitrary and might not match the
2025-03-14T04:33:15.7085618Z the parameter registration or usage order.
2025-03-14T04:33:15.7085638Z 
2025-03-14T04:33:15.7085741Z Arguments:
2025-03-14T04:33:15.7085943Z     params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2025-03-14T04:33:15.7086196Z         or :class:`dict` s giving all parameters, which will be sharded
2025-03-14T04:33:15.7086333Z         across ranks.
2025-03-14T04:33:15.7086338Z 
2025-03-14T04:33:15.7086454Z Keyword Args:
2025-03-14T04:33:15.7086684Z     optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2025-03-14T04:33:15.7086800Z         optimizer.
2025-03-14T04:33:15.7087015Z     process_group (``ProcessGroup``, optional): ``torch.distributed``
2025-03-14T04:33:15.7087235Z         ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2025-03-14T04:33:15.7087395Z         :meth:`torch.distributed.init_process_group`).
2025-03-14T04:33:15.7087645Z     parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2025-03-14T04:33:15.7087866Z         packed into buckets to speed up communication, and ``param.data``
2025-03-14T04:33:15.7088088Z         fields point to bucket views at different offsets; if ``False``,
2025-03-14T04:33:15.7088297Z         each individual parameter is communicated separately, and each
2025-03-14T04:33:15.7088475Z         ``params.data`` stays intact (default: ``False``).
2025-03-14T04:33:15.7088675Z     overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2025-03-14T04:33:15.7088938Z         overlapped with :class:`DistributedDataParallel` 's gradient
2025-03-14T04:33:15.7089157Z         synchronization; this requires (1) either a functional optimizer
2025-03-14T04:33:15.7089358Z         for the ``optimizer_class`` argument or one with a functional
2025-03-14T04:33:15.7089546Z         equivalent and (2) registering a DDP communication hook
2025-03-14T04:33:15.7089770Z         constructed from one of the functions in ``ddp_zero_hook.py``;
2025-03-14T04:33:15.7089949Z         parameters are packed into buckets matching those in
2025-03-14T04:33:15.7090130Z         :class:`DistributedDataParallel`, meaning that the
2025-03-14T04:33:15.7090288Z         ``parameters_as_bucket_view`` argument is ignored.
2025-03-14T04:33:15.7090500Z         If ``False``, :meth:`step` runs disjointly after the backward pass
2025-03-14T04:33:15.7090607Z         (per normal).
2025-03-14T04:33:15.7090732Z         (default: ``False``)
2025-03-14T04:33:15.7090951Z     **defaults: any trailing arguments, which are forwarded to the local
2025-03-14T04:33:15.7091070Z         optimizer.
2025-03-14T04:33:15.7091075Z 
2025-03-14T04:33:15.7091180Z Example::
2025-03-14T04:33:15.7091185Z 
2025-03-14T04:33:15.7091309Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.7091421Z     >>> import torch.nn as nn
2025-03-14T04:33:15.7091681Z     >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2025-03-14T04:33:15.7091891Z     >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-14T04:33:15.7092139Z     >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2025-03-14T04:33:15.7092266Z     >>> ddp = DDP(model, device_ids=[rank])
2025-03-14T04:33:15.7092411Z     >>> opt = ZeroRedundancyOptimizer(
2025-03-14T04:33:15.7092527Z     >>>     ddp.parameters(),
2025-03-14T04:33:15.7092680Z     >>>     optimizer_class=torch.optim.Adam,
2025-03-14T04:33:15.7092779Z     >>>     lr=0.01
2025-03-14T04:33:15.7092888Z     >>> )
2025-03-14T04:33:15.7093006Z     >>> ddp(inputs).sum().backward()
2025-03-14T04:33:15.7093127Z     >>> opt.step()
2025-03-14T04:33:15.7093131Z 
2025-03-14T04:33:15.7093233Z .. warning::
2025-03-14T04:33:15.7093462Z     Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2025-03-14T04:33:15.7093613Z     passed-in parameters are the same dense type.
2025-03-14T04:33:15.7093618Z 
2025-03-14T04:33:15.7093734Z .. warning::
2025-03-14T04:33:15.7093950Z     If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2025-03-14T04:33:15.7094177Z     the way that overlapping :class:`DistributedDataParallel` with
2025-03-14T04:33:15.7094416Z     :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2025-03-14T04:33:15.7094652Z     two or three training iterations do not perform parameter updates in
2025-03-14T04:33:15.7094851Z     the optimizer step, depending on if ``static_graph=False`` or
2025-03-14T04:33:15.7095126Z     ``static_graph=True``, respectively. This is because it needs
2025-03-14T04:33:15.7095323Z     information about the gradient bucketing strategy used by
2025-03-14T04:33:15.7095568Z     :class:`DistributedDataParallel`, which is not finalized until the
2025-03-14T04:33:15.7095777Z     second forward pass if ``static_graph=False`` or until the third
2025-03-14T04:33:15.7096012Z     forward pass if ``static_graph=True``. To adjust for this, one option
2025-03-14T04:33:15.7096132Z     is to prepend dummy inputs.
2025-03-14T04:33:15.7096137Z 
2025-03-14T04:33:15.7096417Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2025-03-14T04:33:15.7096421Z 
2025-03-14T04:33:15.7096564Z .. _ZeRO: https://arxiv.org/abs/1910.02054
2025-03-14T04:33:15.7096569Z 
2025-03-14T04:33:15.7096573Z 
2025-03-14T04:33:15.7096847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.7096852Z 
2025-03-14T04:33:15.7258140Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28.
2025-03-14T04:33:15.7258489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.7258495Z 
2025-03-14T04:33:15.7258748Z Custom reducer class that can be used to specify a custom operation that
2025-03-14T04:33:15.7258935Z reduces losses of multiple microbatches into one value.
2025-03-14T04:33:15.7258942Z 
2025-03-14T04:33:15.7259054Z Example:
2025-03-14T04:33:15.7259159Z >>> # xdoctest: +SKIP
2025-03-14T04:33:15.7259295Z >>> sum_reducer = _CustomReducer(
2025-03-14T04:33:15.7259405Z >>>     torch.tensor(0.0),
2025-03-14T04:33:15.7259531Z >>>     lambda a, b: a + b
2025-03-14T04:33:15.7259624Z >>> )
2025-03-14T04:33:15.7259628Z 
2025-03-14T04:33:15.7259903Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.7259908Z 
2025-03-14T04:33:15.7692994Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6.
2025-03-14T04:33:15.7693991Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.7694381Z 
2025-03-14T04:33:15.7694647Z A decorator for a function indicating that the return value of the function
2025-03-14T04:33:15.7695231Z is guaranteed to be a :class:`~torch.futures.Future` object and this
2025-03-14T04:33:15.7695946Z function can run asynchronously on the RPC callee. More specifically, the
2025-03-14T04:33:15.7696565Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2025-03-14T04:33:15.7697170Z function and installs subsequent processing steps as a callback to that
2025-03-14T04:33:15.7697772Z :class:`~torch.futures.Future`. The installed callback will read the value
2025-03-14T04:33:15.7698345Z from the :class:`~torch.futures.Future` when completed and send the
2025-03-14T04:33:15.7698870Z value back as the RPC response. That also means the returned
2025-03-14T04:33:15.7699415Z :class:`~torch.futures.Future` only exists on the callee side and is never
2025-03-14T04:33:15.7700011Z sent through RPC. This decorator is useful when the wrapped function's
2025-03-14T04:33:15.7700574Z (``fn``) execution needs to pause and resume due to, e.g., containing
2025-03-14T04:33:15.7701137Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2025-03-14T04:33:15.7701481Z 
2025-03-14T04:33:15.7701740Z .. note:: To enable asynchronous execution, applications must pass the
2025-03-14T04:33:15.7702307Z     function object returned by this decorator to RPC APIs. If RPC detected
2025-03-14T04:33:15.7702898Z     attributes installed by this decorator, it knows that this function
2025-03-14T04:33:15.7703438Z     returns a ``Future`` object and will handle that accordingly.
2025-03-14T04:33:15.7703969Z     However, this does not mean this decorator has to be outmost one when
2025-03-14T04:33:15.7704660Z     defining a function. For example, when combined with ``@staticmethod``
2025-03-14T04:33:15.7705241Z     or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2025-03-14T04:33:15.7705812Z     inner decorator to allow the target function be recognized as a static
2025-03-14T04:33:15.7706398Z     or class function. This target function can still execute asynchronously
2025-03-14T04:33:15.7706997Z     because, when accessed, the static or class method preserves attributes
2025-03-14T04:33:15.7707574Z     installed by ``@rpc.functions.async_execution``.
2025-03-14T04:33:15.7707845Z 
2025-03-14T04:33:15.7707849Z 
2025-03-14T04:33:15.7707965Z Example::
2025-03-14T04:33:15.7708318Z     The returned :class:`~torch.futures.Future` object can come from
2025-03-14T04:33:15.7708786Z     :meth:`~torch.distributed.rpc.rpc_async`,
2025-03-14T04:33:15.7709271Z     :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2025-03-14T04:33:15.7709813Z     constructor. The example below shows directly using the
2025-03-14T04:33:15.7710248Z     :class:`~torch.futures.Future` returned by
2025-03-14T04:33:15.7710634Z     :meth:`~torch.futures.Future.then`.
2025-03-14T04:33:15.7710921Z 
2025-03-14T04:33:15.7711050Z     >>> from torch.distributed import rpc
2025-03-14T04:33:15.7711382Z     >>>
2025-03-14T04:33:15.7711642Z     >>> # omitting setup and shutdown RPC
2025-03-14T04:33:15.7711970Z     >>>
2025-03-14T04:33:15.7712210Z     >>> # On all workers
2025-03-14T04:33:15.7712537Z     >>> @rpc.functions.async_execution
2025-03-14T04:33:15.7712885Z     >>> def async_add_chained(to, x, y, z):
2025-03-14T04:33:15.7713312Z     >>>     # This function runs on "worker1" and returns immediately when
2025-03-14T04:33:15.7713828Z     >>>     # the callback is installed through the `then(cb)` API. In the
2025-03-14T04:33:15.7714332Z     >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2025-03-14T04:33:15.7714795Z     >>>     # When the return value of that `rpc_async` arrives at
2025-03-14T04:33:15.7715299Z     >>>     # "worker1", "worker1" will run the lambda function accordingly
2025-03-14T04:33:15.7715812Z     >>>     # and set the value for the previously returned `Future`, which
2025-03-14T04:33:15.7716327Z     >>>     # will then trigger RPC to send the result back to "worker0".
2025-03-14T04:33:15.7716824Z     >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:15.7717228Z     >>>         lambda fut: fut.wait() + z
2025-03-14T04:33:15.7717556Z     >>>     )
2025-03-14T04:33:15.7717838Z     >>>
2025-03-14T04:33:15.7718070Z     >>> # On worker0
2025-03-14T04:33:15.7718345Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.7718640Z     >>> ret = rpc.rpc_sync(
2025-03-14T04:33:15.7718931Z     >>>     "worker1",
2025-03-14T04:33:15.7719289Z     >>>     async_add_chained,
2025-03-14T04:33:15.7719618Z     >>>     args=("worker2", torch.ones(2), 1, 1)
2025-03-14T04:33:15.7719962Z     >>> )
2025-03-14T04:33:15.7720226Z     >>> print(ret)  # prints tensor([3., 3.])
2025-03-14T04:33:15.7720465Z 
2025-03-14T04:33:15.7720719Z     When combined with TorchScript decorators, this decorator must be the
2025-03-14T04:33:15.7721178Z     outmost one.
2025-03-14T04:33:15.7721330Z 
2025-03-14T04:33:15.7721458Z     >>> from torch import Tensor
2025-03-14T04:33:15.7721793Z     >>> from torch.futures import Future
2025-03-14T04:33:15.7722150Z     >>> from torch.distributed import rpc
2025-03-14T04:33:15.7722477Z     >>>
2025-03-14T04:33:15.7722721Z     >>> # omitting setup and shutdown RPC
2025-03-14T04:33:15.7723042Z     >>>
2025-03-14T04:33:15.7723273Z     >>> # On all workers
2025-03-14T04:33:15.7723556Z     >>> @torch.jit.script
2025-03-14T04:33:15.7723890Z     >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2025-03-14T04:33:15.7724253Z     >>>     return x + y
2025-03-14T04:33:15.7724521Z     >>>
2025-03-14T04:33:15.7724773Z     >>> @rpc.functions.async_execution
2025-03-14T04:33:15.7725109Z     >>> @torch.jit.script
2025-03-14T04:33:15.7725527Z     >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2025-03-14T04:33:15.7726020Z     >>>     return rpc.rpc_async(to, script_add, (x, y))
2025-03-14T04:33:15.7726373Z     >>>
2025-03-14T04:33:15.7726611Z     >>> # On worker0
2025-03-14T04:33:15.7726887Z     >>> ret = rpc.rpc_sync(
2025-03-14T04:33:15.7727178Z     >>>     "worker1",
2025-03-14T04:33:15.7727531Z     >>>     async_add,
2025-03-14T04:33:15.7727825Z     >>>     args=("worker2", torch.ones(2), 1)
2025-03-14T04:33:15.7728151Z     >>> )
2025-03-14T04:33:15.7728461Z     >>> print(ret)  # prints tensor([2., 2.])
2025-03-14T04:33:15.7728855Z 
2025-03-14T04:33:15.7729288Z     When combined with static or class method, this decorator must be the
2025-03-14T04:33:15.7729834Z     inner one.
2025-03-14T04:33:15.7729994Z 
2025-03-14T04:33:15.7730122Z     >>> from torch.distributed import rpc
2025-03-14T04:33:15.7730454Z     >>>
2025-03-14T04:33:15.7730711Z     >>> # omitting setup and shutdown RPC
2025-03-14T04:33:15.7731034Z     >>>
2025-03-14T04:33:15.7731267Z     >>> # On all workers
2025-03-14T04:33:15.7731571Z     >>> class AsyncExecutionClass:
2025-03-14T04:33:15.7731877Z     >>>
2025-03-14T04:33:15.7732108Z     >>>     @staticmethod
2025-03-14T04:33:15.7732472Z     >>>     @rpc.functions.async_execution
2025-03-14T04:33:15.7732836Z     >>>     def static_async_add(to, x, y, z):
2025-03-14T04:33:15.7733258Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:15.7733664Z     >>>             lambda fut: fut.wait() + z
2025-03-14T04:33:15.7733997Z     >>>         )
2025-03-14T04:33:15.7734243Z     >>>
2025-03-14T04:33:15.7734472Z     >>>     @classmethod
2025-03-14T04:33:15.7734771Z     >>>     @rpc.functions.async_execution
2025-03-14T04:33:15.7735135Z     >>>     def class_async_add(cls, to, x, y, z):
2025-03-14T04:33:15.7735513Z     >>>         ret_fut = torch.futures.Future()
2025-03-14T04:33:15.7735913Z     >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:15.7736345Z     >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2025-03-14T04:33:15.7736712Z     >>>         )
2025-03-14T04:33:15.7736966Z     >>>         return ret_fut
2025-03-14T04:33:15.7737244Z     >>>
2025-03-14T04:33:15.7737497Z     >>>     @rpc.functions.async_execution
2025-03-14T04:33:15.7737859Z     >>>     def bound_async_add(self, to, x, y, z):
2025-03-14T04:33:15.7738283Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:15.7738695Z     >>>             lambda fut: fut.wait() + z
2025-03-14T04:33:15.7739058Z     >>>         )
2025-03-14T04:33:15.7739287Z     >>>
2025-03-14T04:33:15.7739511Z     >>> # On worker0
2025-03-14T04:33:15.7739781Z     >>> ret = rpc.rpc_sync(
2025-03-14T04:33:15.7740071Z     >>>     "worker1",
2025-03-14T04:33:15.7740377Z     >>>     AsyncExecutionClass.static_async_add,
2025-03-14T04:33:15.7740757Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-14T04:33:15.7741093Z     >>> )
2025-03-14T04:33:15.7741355Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:15.7741694Z     >>>
2025-03-14T04:33:15.7741940Z     >>> ret = rpc.rpc_sync(
2025-03-14T04:33:15.7742225Z     >>>     "worker1",
2025-03-14T04:33:15.7742530Z     >>>     AsyncExecutionClass.class_async_add,
2025-03-14T04:33:15.7742904Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-14T04:33:15.7743237Z     >>> )
2025-03-14T04:33:15.7743493Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:15.7743723Z 
2025-03-14T04:33:15.7743906Z     This decorator also works with RRef helpers, i.e., .
2025-03-14T04:33:15.7744336Z     :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2025-03-14T04:33:15.7744746Z     :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2025-03-14T04:33:15.7745173Z     :meth:`torch.distributed.rpc.RRef.remote`.
2025-03-14T04:33:15.7745440Z 
2025-03-14T04:33:15.7745569Z     >>> from torch.distributed import rpc
2025-03-14T04:33:15.7745903Z     >>>
2025-03-14T04:33:15.7746185Z     >>> # reuse the AsyncExecutionClass class above
2025-03-14T04:33:15.7746646Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-14T04:33:15.7747172Z     >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2025-03-14T04:33:15.7747640Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:15.7748013Z     >>>
2025-03-14T04:33:15.7748449Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-14T04:33:15.7749249Z     >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2025-03-14T04:33:15.7750162Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:15.7750502Z     >>>
2025-03-14T04:33:15.7750794Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-14T04:33:15.7751308Z     >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2025-03-14T04:33:15.7751799Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:15.7752033Z 
2025-03-14T04:33:15.7752304Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.7752677Z 
2025-03-14T04:33:15.7753449Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108.
2025-03-14T04:33:15.7754607Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.7755003Z 
2025-03-14T04:33:15.7755211Z Set device mapping between each RPC caller and callee pair. This
2025-03-14T04:33:15.7755723Z function can be called multiple times to incrementally add
2025-03-14T04:33:15.7756153Z device placement configurations.
2025-03-14T04:33:15.7756374Z 
2025-03-14T04:33:15.7756464Z Args:
2025-03-14T04:33:15.7756694Z     to (str): Callee name.
2025-03-14T04:33:15.7757084Z     device_map (Dict of int, str, or torch.device): Device placement
2025-03-14T04:33:15.7757595Z         mappings from this worker to the callee. This map must be
2025-03-14T04:33:15.7757995Z         invertible.
2025-03-14T04:33:15.7758147Z 
2025-03-14T04:33:15.7758253Z Example:
2025-03-14T04:33:15.7758512Z     >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:15.7758843Z     >>> # both workers
2025-03-14T04:33:15.7759163Z     >>> def add(x, y):
2025-03-14T04:33:15.7759482Z     >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2025-03-14T04:33:15.7759858Z     >>>     return x + y, (x + y).to(2)
2025-03-14T04:33:15.7760174Z     >>>
2025-03-14T04:33:15.7760392Z     >>> # on worker 0
2025-03-14T04:33:15.7760705Z     >>> options = TensorPipeRpcBackendOptions(
2025-03-14T04:33:15.7761132Z     >>>     num_worker_threads=8,
2025-03-14T04:33:15.7761460Z     >>>     device_maps={"worker1": {0: 1}}
2025-03-14T04:33:15.7761828Z     >>> # maps worker0's cuda:0 to worker1's cuda:1
2025-03-14T04:33:15.7762174Z     >>> )
2025-03-14T04:33:15.7762448Z     >>> options.set_device_map("worker1", {1: 2})
2025-03-14T04:33:15.7762840Z     >>> # maps worker0's cuda:1 to worker1's cuda:2
2025-03-14T04:33:15.7763184Z     >>>
2025-03-14T04:33:15.7763417Z     >>> rpc.init_rpc(
2025-03-14T04:33:15.7763687Z     >>>     "worker0",
2025-03-14T04:33:15.7763951Z     >>>     rank=0,
2025-03-14T04:33:15.7764207Z     >>>     world_size=2,
2025-03-14T04:33:15.7764524Z     >>>     backend=rpc.BackendType.TENSORPIPE,
2025-03-14T04:33:15.7764896Z     >>>     rpc_backend_options=options
2025-03-14T04:33:15.7765214Z     >>> )
2025-03-14T04:33:15.7765423Z     >>>
2025-03-14T04:33:15.7765657Z     >>> x = torch.ones(2)
2025-03-14T04:33:15.7766001Z     >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2025-03-14T04:33:15.7766475Z     >>> # The first argument will be moved to cuda:1 on worker1. When
2025-03-14T04:33:15.7766973Z     >>> # sending the return value back, it will follow the invert of
2025-03-14T04:33:15.7767465Z     >>> # the device map, and hence will be moved back to cuda:0 and
2025-03-14T04:33:15.7767868Z     >>> # cuda:1 on worker0
2025-03-14T04:33:15.7768207Z     >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2025-03-14T04:33:15.7768626Z     >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2025-03-14T04:33:15.7769016Z 
2025-03-14T04:33:15.7769278Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.7769665Z 
2025-03-14T04:33:15.7770400Z msg = Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19.
2025-03-14T04:33:15.7771518Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.7771911Z 
2025-03-14T04:33:15.7772114Z It has the same API as ``torch.autograd.profiler.profile`` class,
2025-03-14T04:33:15.7772713Z except that it enables profiling on all threads running RPC server request callbacks.
2025-03-14T04:33:15.7773116Z 
2025-03-14T04:33:15.7773398Z Context manager that manages autograd profiler state and holds a summary of results.
2025-03-14T04:33:15.7774040Z Under the hood it just records events of functions being executed in C++ and
2025-03-14T04:33:15.7774631Z exposes those events to Python. You can wrap any code into it and it will
2025-03-14T04:33:15.7775124Z only report runtime of PyTorch functions.
2025-03-14T04:33:15.7775639Z Note: profiler is thread local and is automatically propagated into the async tasks
2025-03-14T04:33:15.7776056Z 
2025-03-14T04:33:15.7776160Z Args:
2025-03-14T04:33:15.7776563Z     enabled (bool, optional): Setting this to False makes this context manager a no-op.
2025-03-14T04:33:15.7777063Z         Default: ``True``.
2025-03-14T04:33:15.7777242Z 
2025-03-14T04:33:15.7777544Z     use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
2025-03-14T04:33:15.7778165Z         Adds approximately 4us of overhead to each tensor operation.
2025-03-14T04:33:15.7778593Z         Default: ``False``
2025-03-14T04:33:15.7778768Z 
2025-03-14T04:33:15.7779017Z     record_shapes (bool, optional): If shapes recording is set, information
2025-03-14T04:33:15.7779609Z         about input dimensions will be collected. This allows one to see which
2025-03-14T04:33:15.7780187Z         dimensions have been used under the hood and further group by them
2025-03-14T04:33:15.7780749Z         using prof.key_averages(group_by_input_shape=True). Please note that
2025-03-14T04:33:15.7781335Z         shape recording might skew your profiling data. It is recommended to
2025-03-14T04:33:15.7781923Z         use separate runs with and without shape recording to validate the timing.
2025-03-14T04:33:15.7782553Z         Most likely the skew will be negligible for bottom most events (in a case
2025-03-14T04:33:15.7783126Z         of nested function calls). But for higher level functions the total
2025-03-14T04:33:15.7783678Z         self cpu time might be artificially increased because of the shape
2025-03-14T04:33:15.7784092Z         collection.
2025-03-14T04:33:15.7784265Z 
2025-03-14T04:33:15.7784540Z     profile_memory (bool, optional): Whether to report memory usage, default: ``False``
2025-03-14T04:33:15.7784942Z 
2025-03-14T04:33:15.7785038Z .. warning:
2025-03-14T04:33:15.7785402Z     Enabling memory profiling incurs additional profiler overhead
2025-03-14T04:33:15.7785732Z 
2025-03-14T04:33:15.7785827Z .. warning:
2025-03-14T04:33:15.7786224Z     Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_),
2025-03-14T04:33:15.7786800Z     one cannot use the profiler with ``use_cuda = True`` to benchmark
2025-03-14T04:33:15.7787373Z     DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
2025-03-14T04:33:15.7787909Z     please use ``use_cuda = False`` or ``num_workers = 0``.
2025-03-14T04:33:15.7788205Z 
2025-03-14T04:33:15.7788299Z Example:
2025-03-14T04:33:15.7788546Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:15.7788835Z     >>> # On worker 0:
2025-03-14T04:33:15.7789108Z     >>> import torch
2025-03-14T04:33:15.7789416Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.7789816Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:15.7790214Z     >>> x, y = torch.tensor(1), torch.tensor(2)
2025-03-14T04:33:15.7790648Z     >>> outer_profile_rref = rpc.remote(
2025-03-14T04:33:15.7791051Z     ...     dst_worker_name, rpc._server_process_global_profile
2025-03-14T04:33:15.7791438Z     ... )
2025-03-14T04:33:15.7791712Z     >>> outer_profile_rref.rpc_sync().__enter__()
2025-03-14T04:33:15.7792123Z     >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y))
2025-03-14T04:33:15.7792506Z     >>> inner_profile_rref = rpc.remote(
2025-03-14T04:33:15.7792906Z     ...     dst_worker_name, rpc._server_process_global_profile
2025-03-14T04:33:15.7793282Z     ... )
2025-03-14T04:33:15.7793550Z     >>> inner_profile_rref.rpc_sync().__enter__()
2025-03-14T04:33:15.7793954Z     >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y))
2025-03-14T04:33:15.7794401Z     >>> inner_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-14T04:33:15.7794876Z     >>> outer_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-14T04:33:15.7795337Z     >>> print(inner_profile_rref.rpc_sync().key_averages())
2025-03-14T04:33:15.7795864Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:15.7796571Z     Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-14T04:33:15.7797239Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:15.7797799Z     sub        85.06%           76.275us         100.00%          89.667us         89.667us         1
2025-03-14T04:33:15.7798312Z     empty      14.94%           13.392us         14.94%           13.392us         13.392us         1
2025-03-14T04:33:15.7798874Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:15.7799464Z     Self CPU time total: 89.667us
2025-03-14T04:33:15.7799847Z     >>> print(outer_profile_rref.rpc_sync().key_averages())
2025-03-14T04:33:15.7800367Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:15.7801037Z     Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-14T04:33:15.7801703Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:15.7802280Z     sub        35.65%           76.275us         41.91%           89.667us         89.667us         1
2025-03-14T04:33:15.7802787Z     empty      12.67%           27.101us         12.67%           27.101us         13.551us         2
2025-03-14T04:33:15.7803297Z     add        51.68%           110.550us        58.09%           124.259us        124.259us        1
2025-03-14T04:33:15.7803848Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:15.7804326Z     Self CPU time total: 213.926us
2025-03-14T04:33:15.7804660Z     >>> rpc.shutdown()
2025-03-14T04:33:15.7804826Z 
2025-03-14T04:33:15.7804941Z     >>> # On worker 1:
2025-03-14T04:33:15.7805248Z     >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:15.7805642Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:15.7806069Z     >>> # wait for worker 0 to finish work, and then shutdown.
2025-03-14T04:33:15.7806451Z     >>> rpc.shutdown()
2025-03-14T04:33:15.7806613Z 
2025-03-14T04:33:15.7806888Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.7807258Z 
2025-03-14T04:33:15.8691308Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=34.
2025-03-14T04:33:15.8692356Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.8692739Z 
2025-03-14T04:33:15.8693022Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2025-03-14T04:33:15.8693914Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2025-03-14T04:33:15.8694594Z the local components of :class:`DTensor`, call the function, and wrap the outputs to
2025-03-14T04:33:15.8695161Z :class:`DTensor` according to the ``out_placements``.
2025-03-14T04:33:15.8695428Z 
2025-03-14T04:33:15.8695536Z Args:
2025-03-14T04:33:15.8695882Z     func (Callable): the function to be applied on each local shard of
2025-03-14T04:33:15.8696330Z         :class:`DTensor` s.
2025-03-14T04:33:15.8696752Z     out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2025-03-14T04:33:15.8697364Z         the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2025-03-14T04:33:15.8697991Z         If the flattened ``output`` is a single value, the ``out_placements`` should be
2025-03-14T04:33:15.8698604Z         of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2025-03-14T04:33:15.8699227Z         values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2025-03-14T04:33:15.8699729Z         mapping to the flattened ``output``.
2025-03-14T04:33:15.8700227Z         Besides, for :class:`Tensor` output, we use `PlacementType` as its
2025-03-14T04:33:15.8700822Z         placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2025-03-14T04:33:15.8701333Z         should be `None`.
2025-03-14T04:33:15.8701854Z         Note that the only exception is when no :class:`DTensor` argument is passed
2025-03-14T04:33:15.8702523Z         in. In this case, even if `out_placements` is not `None`, the result function
2025-03-14T04:33:15.8703514Z         should ignore the desired placements because the function is not running with
2025-03-14T04:33:15.8704122Z         :class:`DTensor` s.
2025-03-14T04:33:15.8704485Z     in_placements (Tuple[`PlacementType`, ...], optional):
2025-03-14T04:33:15.8705065Z         the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2025-03-14T04:33:15.8705723Z         If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2025-03-14T04:33:15.8706331Z         placements of each :class:`DTensor` argument is the same as the required
2025-03-14T04:33:15.8706879Z         placements or not. If the placements are not the same and
2025-03-14T04:33:15.8707433Z         ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2025-03-14T04:33:15.8708212Z         ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2025-03-14T04:33:15.8708857Z         the required sharding placements before passing its local tensor to ``func``.
2025-03-14T04:33:15.8709476Z         The only exception is when required placements are not ``None`` and the
2025-03-14T04:33:15.8710075Z         argument is a :class:`torch.Tensor`. In this case, the placements examination
2025-03-14T04:33:15.8710676Z         will be skipped and the argument will be directly passed to ``func``.
2025-03-14T04:33:15.8711259Z         If ``in_placements`` is ``None``, no placements examination will be performed.
2025-03-14T04:33:15.8711721Z         Default: None
2025-03-14T04:33:15.8712043Z     device_mesh (:class:`DeviceMesh`, optional):
2025-03-14T04:33:15.8712524Z         the device mesh that all the :class:`DTensor` s are placed on. If not
2025-03-14T04:33:15.8713109Z         specified, this will be inferred from the input :class:`DTensor` s' device
2025-03-14T04:33:15.8713723Z         mesh. `local_map` requires every :class:`DTensor` s to be placed on the same
2025-03-14T04:33:15.8714210Z         device mesh. Default: None.
2025-03-14T04:33:15.8714562Z     redistribute_inputs (bool, optional):
2025-03-14T04:33:15.8715064Z         the bool value indicating whether to reshard the input :class:`DTensor` s when
2025-03-14T04:33:15.8715704Z         their placements are different from the required input placements. If this
2025-03-14T04:33:15.8716356Z         value is ``False`` and some :class:`DTensor` input has a different placement,
2025-03-14T04:33:15.8716896Z         an exception will be raised. Default: False.
2025-03-14T04:33:15.8717150Z 
2025-03-14T04:33:15.8717250Z Returns:
2025-03-14T04:33:15.8717654Z     A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2025-03-14T04:33:15.8718289Z     and returns a :class:`DTensor` constructed from the return value of ``func``.
2025-03-14T04:33:15.8718668Z 
2025-03-14T04:33:15.8718759Z Raises:
2025-03-14T04:33:15.8719236Z     AssertionError: If the input :class:`DTensor` is not placed on the same device
2025-03-14T04:33:15.8719866Z         mesh, or if they are placed on a different device mesh than the ``device_mesh``
2025-03-14T04:33:15.8720355Z         argument passed in.
2025-03-14T04:33:15.8720556Z 
2025-03-14T04:33:15.8720803Z     AssertionError: For any non-DTensor output, we require its corresponding
2025-03-14T04:33:15.8721438Z         output placement in ``out_placements`` be None. An AssertionError will be raised
2025-03-14T04:33:15.8721941Z         if this is not the case.
2025-03-14T04:33:15.8722156Z 
2025-03-14T04:33:15.8722423Z     ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2025-03-14T04:33:15.8723008Z         a redistribution according to ``in_placements``.
2025-03-14T04:33:15.8723284Z 
2025-03-14T04:33:15.8723378Z Example:
2025-03-14T04:33:15.8723632Z     >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:15.8724011Z     >>> def mm_allreduce_forward(device_mesh, W, X):
2025-03-14T04:33:15.8724399Z     >>>     partial_sum_tensor = torch.mm(W, X)
2025-03-14T04:33:15.8724888Z     >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2025-03-14T04:33:15.8725368Z     >>>     return reduced_tensor
2025-03-14T04:33:15.8725668Z     >>>
2025-03-14T04:33:15.8725934Z     >>> W = torch.randn(12, 8, requires_grad=False)
2025-03-14T04:33:15.8726317Z     >>> X = torch.randn(8, 16, requires_grad=False)
2025-03-14T04:33:15.8726671Z     >>> Y = torch.mm(W, X)
2025-03-14T04:33:15.8727059Z     >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2025-03-14T04:33:15.8727574Z     >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2025-03-14T04:33:15.8727986Z     >>>
2025-03-14T04:33:15.8728376Z     >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
2025-03-14T04:33:15.8728907Z     >>> local_mm_allreduce_forward = local_map(
2025-03-14T04:33:15.8729264Z     >>>     mm_allreduce_forward,
2025-03-14T04:33:15.8729625Z     >>>     out_placements=[Replicate()],
2025-03-14T04:33:15.8729990Z     >>>     in_placements=[col_wise, row_wise],
2025-03-14T04:33:15.8730345Z     >>>     device_mesh=device_mesh,
2025-03-14T04:33:15.8730654Z     >>> )
2025-03-14T04:33:15.8730879Z     >>>
2025-03-14T04:33:15.8731121Z     >>> W_dt = distribute_tensor(
2025-03-14T04:33:15.8731451Z     ...     W, device_mesh, (col_wise)
2025-03-14T04:33:15.8731798Z     ... )  # col-wisely sharded W tensor
2025-03-14T04:33:15.8732145Z     >>> X_dt = distribute_tensor(
2025-03-14T04:33:15.8732466Z     ...     X, device_mesh, (row_wise)
2025-03-14T04:33:15.8732800Z     ... )  # row-wisely sharded X tensor
2025-03-14T04:33:15.8733150Z     >>> Y_dt = local_mm_allreduce_forward(
2025-03-14T04:33:15.8733489Z     ...     device_mesh, W_dt, X_dt
2025-03-14T04:33:15.8733841Z     ... )  # apply local_mm_allreduce_forward to DTensors
2025-03-14T04:33:15.8734113Z 
2025-03-14T04:33:15.8734336Z .. note:: This API is currently experimental and subject to change
2025-03-14T04:33:15.8734654Z 
2025-03-14T04:33:15.8734908Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.8735289Z 
2025-03-14T04:33:15.8736040Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=26.
2025-03-14T04:33:15.8737140Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.8737582Z 
2025-03-14T04:33:15.8737908Z :meth:`register_sharding` is an experimental API that allows users to register sharding
2025-03-14T04:33:15.8738559Z strategies for an operator when the tensor inputs and outputs are DTensor.
2025-03-14T04:33:15.8739180Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2025-03-14T04:33:15.8739807Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2025-03-14T04:33:15.8740456Z when users would like to overwrite default sharding strategies of existing operators.
2025-03-14T04:33:15.8740849Z 
2025-03-14T04:33:15.8740955Z Args:
2025-03-14T04:33:15.8741221Z     op (Union[OpOverload, List[OpOverload]]):
2025-03-14T04:33:15.8741667Z         An op or a list of ops to register the customized sharding function.
2025-03-14T04:33:15.8741979Z 
2025-03-14T04:33:15.8742070Z Returns:
2025-03-14T04:33:15.8742474Z     A function decorator which can be used to wrap a function that defines the sharding
2025-03-14T04:33:15.8743151Z     strategy for the operator specified in ``op``. The defined sharding strategy will be
2025-03-14T04:33:15.8743823Z     registered to DTensor and will override the default sharding strategy if DTensor has
2025-03-14T04:33:15.8744557Z     already implemented the operator. The customized sharding function takes the same inputs
2025-03-14T04:33:15.8745228Z     as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2025-03-14T04:33:15.8745873Z     replaced by a tensor-like object that DTensor uses internally). The function should
2025-03-14T04:33:15.8746546Z     return a sequence of 2-tuples, each specifying acceptable output placements and its
2025-03-14T04:33:15.8747071Z     corresponding intput placements.
2025-03-14T04:33:15.8747304Z 
2025-03-14T04:33:15.8747398Z Example:
2025-03-14T04:33:15.8747652Z     >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:15.8748026Z     >>> @register_sharding(aten._softmax.default)
2025-03-14T04:33:15.8748449Z     >>> def custom_softmax_sharding(x, dim, half_to_float):
2025-03-14T04:33:15.8749075Z     >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2025-03-14T04:33:15.8749465Z     >>>     acceptable_shardings = []
2025-03-14T04:33:15.8749789Z     >>>
2025-03-14T04:33:15.8750107Z     >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2025-03-14T04:33:15.8750563Z     >>>     acceptable_shardings.append(all_replicate)
2025-03-14T04:33:15.8750922Z     >>>
2025-03-14T04:33:15.8751242Z     >>>     for sharding_dim in range(x.ndim):
2025-03-14T04:33:15.8751629Z     >>>         if sharding_dim != softmax_dim:
2025-03-14T04:33:15.8751981Z     >>>             all_sharded = (
2025-03-14T04:33:15.8752301Z     >>>                 [Shard(sharding_dim)],
2025-03-14T04:33:15.8752673Z     >>>                 [Shard(sharding_dim), None, None],
2025-03-14T04:33:15.8753027Z     >>>             )
2025-03-14T04:33:15.8753346Z     >>>             acceptable_shardings.append(all_sharded)
2025-03-14T04:33:15.8753709Z     >>>
2025-03-14T04:33:15.8753974Z     >>>     return acceptable_shardings
2025-03-14T04:33:15.8754220Z 
2025-03-14T04:33:15.8754426Z .. note:: This API is currently experimental and subject to change
2025-03-14T04:33:15.8754752Z 
2025-03-14T04:33:15.8755010Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.8755397Z 
2025-03-14T04:33:15.8905198Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=403.
2025-03-14T04:33:15.8906240Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.8906638Z 
2025-03-14T04:33:15.8907022Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2025-03-14T04:33:15.8907857Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2025-03-14T04:33:15.8908309Z 
2025-03-14T04:33:15.8908442Z Keyword Args:
2025-03-14T04:33:15.8909019Z     input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-14T04:33:15.8910071Z         The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-03-14T04:33:15.8910967Z         DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-03-14T04:33:15.8911579Z         as a placeholder. default: None.
2025-03-14T04:33:15.8912048Z     desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-14T04:33:15.8912785Z         The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-14T04:33:15.8913675Z         have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-03-14T04:33:15.8914328Z     input_kwarg_layouts (Dict[str, Placement]):
2025-03-14T04:33:15.8914962Z         The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-03-14T04:33:15.8915563Z         default: None
2025-03-14T04:33:15.8915900Z     desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-03-14T04:33:15.8916608Z         The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-14T04:33:15.8917252Z         have the desired DTensor layouts. default: None.
2025-03-14T04:33:15.8917650Z     use_local_output (bool, optional):
2025-03-14T04:33:15.8918245Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-03-14T04:33:15.8918823Z Returns:
2025-03-14T04:33:15.8919334Z     A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2025-03-14T04:33:15.8919774Z 
2025-03-14T04:33:15.8919898Z Example::
2025-03-14T04:33:15.8920153Z     >>> # xdoctest: +SKIP(failing)
2025-03-14T04:33:15.8920710Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2025-03-14T04:33:15.8921351Z     >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-14T04:33:15.8921765Z     >>> ...
2025-03-14T04:33:15.8922211Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-14T04:33:15.8922778Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-14T04:33:15.8923121Z     >>>
2025-03-14T04:33:15.8923643Z     >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2025-03-14T04:33:15.8924256Z     >>> # and then redistributed to Replicated DTensor.
2025-03-14T04:33:15.8924638Z     >>> parallelize_module(
2025-03-14T04:33:15.8924961Z     >>>     block, # this can be a submodule or module
2025-03-14T04:33:15.8925319Z     >>>     tp_mesh,
2025-03-14T04:33:15.8925601Z     >>>     parallelize_plan={
2025-03-14T04:33:15.8925927Z     >>>         "attn": PrepareModuleInput(
2025-03-14T04:33:15.8926314Z     >>>             input_layouts=(Shard(0), None, None, ...),
2025-03-14T04:33:15.8926753Z     >>>             desired_input_layouts=(Replicate(), None, None, ...)
2025-03-14T04:33:15.8927150Z     >>>         ),
2025-03-14T04:33:15.8927396Z     >>>     }
2025-03-14T04:33:15.8927629Z     >>> )
2025-03-14T04:33:15.8927752Z 
2025-03-14T04:33:15.8928023Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.8928391Z 
2025-03-14T04:33:15.8929060Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=562.
2025-03-14T04:33:15.8930091Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.8930481Z 
2025-03-14T04:33:15.8930872Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2025-03-14T04:33:15.8931754Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2025-03-14T04:33:15.8932238Z 
2025-03-14T04:33:15.8932336Z Keyword Args:
2025-03-14T04:33:15.8932661Z     output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-14T04:33:15.8933290Z         The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-03-14T04:33:15.8934135Z         DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-03-14T04:33:15.8934784Z         ``None`` need to be specified as a placeholder.
2025-03-14T04:33:15.8935248Z     desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-14T04:33:15.8935956Z         The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-03-14T04:33:15.8936581Z         have the desired DTensor layouts.
2025-03-14T04:33:15.8936935Z     use_local_output (bool, optional):
2025-03-14T04:33:15.8937527Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-03-14T04:33:15.8938101Z Returns:
2025-03-14T04:33:15.8938530Z     A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2025-03-14T04:33:15.8938975Z 
2025-03-14T04:33:15.8939086Z Example::
2025-03-14T04:33:15.8939335Z     >>> # xdoctest: +SKIP(failing)
2025-03-14T04:33:15.8939866Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2025-03-14T04:33:15.8940491Z     >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-14T04:33:15.8940900Z     >>> ...
2025-03-14T04:33:15.8941350Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-14T04:33:15.8941921Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-14T04:33:15.8942267Z     >>>
2025-03-14T04:33:15.8942805Z     >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2025-03-14T04:33:15.8943477Z     >>> # and then redistributed to Sharded DTensor.
2025-03-14T04:33:15.8943846Z     >>> parallelize_module(
2025-03-14T04:33:15.8944184Z     >>>     block, # this can be a submodule or module
2025-03-14T04:33:15.8944538Z     >>>     tp_mesh,
2025-03-14T04:33:15.8944846Z     >>>     parallelize_plan = PrepareModuleOutput(
2025-03-14T04:33:15.8945231Z     >>>         output_layouts=Replicate(),
2025-03-14T04:33:15.8945595Z     >>>         desired_output_layouts=Shard(0)
2025-03-14T04:33:15.8945958Z     >>>     )
2025-03-14T04:33:15.8946193Z     >>> )
2025-03-14T04:33:15.8946318Z 
2025-03-14T04:33:15.8946591Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.8946963Z 
2025-03-14T04:33:15.9429838Z msg = Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=55.
2025-03-14T04:33:15.9430978Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.9431364Z 
2025-03-14T04:33:15.9431690Z Creates a multivariate normal distribution with covariance matrix having a low-rank form
2025-03-14T04:33:15.9432343Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
2025-03-14T04:33:15.9432639Z 
2025-03-14T04:33:15.9432841Z     covariance_matrix = cov_factor @ cov_factor.T + cov_diag
2025-03-14T04:33:15.9433133Z 
2025-03-14T04:33:15.9433247Z Example:
2025-03-14T04:33:15.9433546Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-03-14T04:33:15.9433964Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:15.9434359Z     >>> m = LowRankMultivariateNormal(
2025-03-14T04:33:15.9434785Z     ...     torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2)
2025-03-14T04:33:15.9435191Z     ... )
2025-03-14T04:33:15.9435620Z     >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
2025-03-14T04:33:15.9436149Z     tensor([-0.2102, -0.5429])
2025-03-14T04:33:15.9436522Z 
2025-03-14T04:33:15.9436629Z Args:
2025-03-14T04:33:15.9436989Z     loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
2025-03-14T04:33:15.9437622Z     cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
2025-03-14T04:33:15.9438141Z         `batch_shape + event_shape + (rank,)`
2025-03-14T04:33:15.9438651Z     cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
2025-03-14T04:33:15.9439219Z         `batch_shape + event_shape`
2025-03-14T04:33:15.9439444Z 
2025-03-14T04:33:15.9439537Z Note:
2025-03-14T04:33:15.9439939Z     The computation for determinant and inverse of covariance matrix is avoided when
2025-03-14T04:33:15.9440581Z     `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
2025-03-14T04:33:15.9441167Z     <https://en.wikipedia.org/wiki/Woodbury_matrix_identity>`_ and
2025-03-14T04:33:15.9441793Z     `matrix determinant lemma <https://en.wikipedia.org/wiki/Matrix_determinant_lemma>`_.
2025-03-14T04:33:15.9442467Z     Thanks to these formulas, we just need to compute the determinant and inverse of
2025-03-14T04:33:15.9443031Z     the small size "capacitance" matrix::
2025-03-14T04:33:15.9443274Z 
2025-03-14T04:33:15.9443463Z         capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
2025-03-14T04:33:15.9443776Z 
2025-03-14T04:33:15.9444031Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.9444423Z 
2025-03-14T04:33:15.9447691Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13.
2025-03-14T04:33:15.9447975Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.9447980Z 
2025-03-14T04:33:15.9448206Z The `MixtureSameFamily` distribution implements a (batch of) mixture
2025-03-14T04:33:15.9448472Z distribution where all component are from different parameterizations of
2025-03-14T04:33:15.9448826Z the same distribution type. It is parameterized by a `Categorical`
2025-03-14T04:33:15.9449051Z "selecting distribution" (over `k` component) and a component
2025-03-14T04:33:15.9449267Z distribution, i.e., a `Distribution` with a rightmost batch shape
2025-03-14T04:33:15.9449449Z (equal to `[k]`) which indexes each (batch of) component.
2025-03-14T04:33:15.9449453Z 
2025-03-14T04:33:15.9449558Z Examples::
2025-03-14T04:33:15.9449563Z 
2025-03-14T04:33:15.9449802Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:15.9450016Z     >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2025-03-14T04:33:15.9450167Z     >>> # weighted normal distributions
2025-03-14T04:33:15.9450290Z     >>> mix = D.Categorical(torch.ones(5,))
2025-03-14T04:33:15.9450459Z     >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2025-03-14T04:33:15.9450585Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-03-14T04:33:15.9450590Z 
2025-03-14T04:33:15.9450817Z     >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2025-03-14T04:33:15.9450953Z     >>> # weighted bivariate normal distributions
2025-03-14T04:33:15.9451095Z     >>> mix = D.Categorical(torch.ones(5,))
2025-03-14T04:33:15.9451215Z     >>> comp = D.Independent(D.Normal(
2025-03-14T04:33:15.9451365Z     ...          torch.randn(5,2), torch.rand(5,2)), 1)
2025-03-14T04:33:15.9451493Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-03-14T04:33:15.9451497Z 
2025-03-14T04:33:15.9451699Z     >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2025-03-14T04:33:15.9451905Z     >>> # consisting of 5 random weighted bivariate normal distributions
2025-03-14T04:33:15.9452045Z     >>> mix = D.Categorical(torch.rand(3,5))
2025-03-14T04:33:15.9452164Z     >>> comp = D.Independent(D.Normal(
2025-03-14T04:33:15.9452323Z     ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2025-03-14T04:33:15.9452448Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-03-14T04:33:15.9452513Z 
2025-03-14T04:33:15.9452669Z Args:
2025-03-14T04:33:15.9452882Z     mixture_distribution: `torch.distributions.Categorical`-like
2025-03-14T04:33:15.9453096Z         instance. Manages the probability of selecting component.
2025-03-14T04:33:15.9453275Z         The number of categories must match the rightmost batch
2025-03-14T04:33:15.9453487Z         dimension of the `component_distribution`. Must have either
2025-03-14T04:33:15.9453636Z         scalar `batch_shape` or `batch_shape` matching
2025-03-14T04:33:15.9453798Z         `component_distribution.batch_shape[:-1]`
2025-03-14T04:33:15.9454023Z     component_distribution: `torch.distributions.Distribution`-like
2025-03-14T04:33:15.9454225Z         instance. Right-most batch dimension indexes component.
2025-03-14T04:33:15.9454230Z 
2025-03-14T04:33:15.9454487Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.9454492Z 
2025-03-14T04:33:15.9555975Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=111.
2025-03-14T04:33:15.9556264Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.9556336Z 
2025-03-14T04:33:15.9556529Z Creates a RelaxedBernoulli distribution, parametrized by
2025-03-14T04:33:15.9556741Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2025-03-14T04:33:15.9556965Z (but not both). This is a relaxed version of the `Bernoulli` distribution,
2025-03-14T04:33:15.9557169Z so the values are in (0, 1), and has reparametrizable samples.
2025-03-14T04:33:15.9557174Z 
2025-03-14T04:33:15.9557276Z Example::
2025-03-14T04:33:15.9557280Z 
2025-03-14T04:33:15.9557446Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:15.9557582Z     >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2025-03-14T04:33:15.9557730Z     ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2025-03-14T04:33:15.9557826Z     >>> m.sample()
2025-03-14T04:33:15.9557967Z     tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2025-03-14T04:33:15.9557971Z 
2025-03-14T04:33:15.9558061Z Args:
2025-03-14T04:33:15.9558220Z     temperature (Tensor): relaxation temperature
2025-03-14T04:33:15.9558397Z     probs (Number, Tensor): the probability of sampling `1`
2025-03-14T04:33:15.9558578Z     logits (Number, Tensor): the log-odds of sampling `1`
2025-03-14T04:33:15.9558582Z 
2025-03-14T04:33:15.9558878Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.9558883Z 
2025-03-14T04:33:15.9569631Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=101.
2025-03-14T04:33:15.9569904Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:15.9569909Z 
2025-03-14T04:33:15.9570145Z Creates a RelaxedOneHotCategorical distribution parametrized by
2025-03-14T04:33:15.9570359Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2025-03-14T04:33:15.9570619Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2025-03-14T04:33:15.9570791Z its samples are on simplex, and are reparametrizable.
2025-03-14T04:33:15.9570812Z 
2025-03-14T04:33:15.9570912Z Example::
2025-03-14T04:33:15.9570916Z 
2025-03-14T04:33:15.9571074Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:15.9571237Z     >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2025-03-14T04:33:15.9571371Z     ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2025-03-14T04:33:15.9571482Z     >>> m.sample()
2025-03-14T04:33:15.9571604Z     tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2025-03-14T04:33:15.9571622Z 
2025-03-14T04:33:15.9571712Z Args:
2025-03-14T04:33:15.9571856Z     temperature (Tensor): relaxation temperature
2025-03-14T04:33:15.9571992Z     probs (Tensor): event probabilities
2025-03-14T04:33:15.9572240Z     logits (Tensor): unnormalized log probability for each event
2025-03-14T04:33:15.9572295Z 
2025-03-14T04:33:15.9572551Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:15.9572558Z 
2025-03-14T04:33:16.2739039Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245.
2025-03-14T04:33:16.2740353Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.2741236Z Return a new dict with new, potentially nested, key value pair
2025-03-14T04:33:16.2741668Z 
2025-03-14T04:33:16.2741785Z     >>> purchase = {
2025-03-14T04:33:16.2742060Z     ...     "name": "Alice",
2025-03-14T04:33:16.2742647Z     ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-14T04:33:16.2743302Z     ...     "credit card": "5555-1234-1234-1234",
2025-03-14T04:33:16.2743647Z     ... }
2025-03-14T04:33:16.2744006Z     >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2025-03-14T04:33:16.2744580Z     {'credit card': '5555-1234-1234-1234',
2025-03-14T04:33:16.2744912Z      'name': 'Alice',
2025-03-14T04:33:16.2745705Z      'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2025-03-14T04:33:16.2746196Z     
2025-03-14T04:33:16.2746588Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.2746958Z 
2025-03-14T04:33:16.2747666Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261.
2025-03-14T04:33:16.2748839Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.2749389Z Update value in a (potentially) nested dictionary
2025-03-14T04:33:16.2749655Z 
2025-03-14T04:33:16.2749768Z     inputs:
2025-03-14T04:33:16.2750043Z     d - dictionary on which to operate
2025-03-14T04:33:16.2750509Z     keys - list or tuple giving the location of the value to be changed in d
2025-03-14T04:33:16.2750991Z     func - function to operate on that value
2025-03-14T04:33:16.2751227Z 
2025-03-14T04:33:16.2751442Z     If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2025-03-14T04:33:16.2752007Z     original dictionary with v replaced by func(v), but does not mutate the
2025-03-14T04:33:16.2752481Z     original dictionary.
2025-03-14T04:33:16.2752660Z 
2025-03-14T04:33:16.2752969Z     If k0 is not a key in d, update_in creates nested dictionaries to the depth
2025-03-14T04:33:16.2753528Z     specified by the keys, with the innermost value set to func(default).
2025-03-14T04:33:16.2753865Z 
2025-03-14T04:33:16.2753989Z     >>> inc = lambda x: x + 1
2025-03-14T04:33:16.2754309Z     >>> update_in({"a": 0}, ["a"], inc)
2025-03-14T04:33:16.2754613Z     {'a': 1}
2025-03-14T04:33:16.2754764Z 
2025-03-14T04:33:16.2754865Z     >>> transaction = {
2025-03-14T04:33:16.2755144Z     ...     "name": "Alice",
2025-03-14T04:33:16.2755531Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-14T04:33:16.2755973Z     ...     "credit card": "5555-1234-1234-1234",
2025-03-14T04:33:16.2756302Z     ... }
2025-03-14T04:33:16.2756656Z     >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2025-03-14T04:33:16.2757111Z     {'credit card': '5555-1234-1234-1234',
2025-03-14T04:33:16.2757436Z      'name': 'Alice',
2025-03-14T04:33:16.2757775Z      'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2025-03-14T04:33:16.2758074Z 
2025-03-14T04:33:16.2758201Z     >>> # updating a value when k0 is not in d
2025-03-14T04:33:16.2758581Z     >>> update_in({}, [1, 2, 3], str, default="bar")
2025-03-14T04:33:16.2758933Z     {1: {2: {3: 'bar'}}}
2025-03-14T04:33:16.2759284Z     >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2025-03-14T04:33:16.2759631Z     {1: 'foo', 2: {3: {4: 1}}}
2025-03-14T04:33:16.2759914Z     
2025-03-14T04:33:16.2760299Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.2760809Z 
2025-03-14T04:33:16.2761460Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320.
2025-03-14T04:33:16.2762476Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.2763036Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2025-03-14T04:33:16.2763315Z 
2025-03-14T04:33:16.2763518Z     If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2025-03-14T04:33:16.2764042Z     ``no_default`` is specified, then it raises KeyError or IndexError.
2025-03-14T04:33:16.2764357Z 
2025-03-14T04:33:16.2764588Z     ``get_in`` is a generalization of ``operator.getitem`` for nested data
2025-03-14T04:33:16.2765060Z     structures such as dictionaries and lists.
2025-03-14T04:33:16.2765305Z 
2025-03-14T04:33:16.2765422Z     >>> transaction = {
2025-03-14T04:33:16.2765687Z     ...     "name": "Alice",
2025-03-14T04:33:16.2766077Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-14T04:33:16.2766516Z     ...     "credit card": "5555-1234-1234-1234",
2025-03-14T04:33:16.2766891Z     ... }
2025-03-14T04:33:16.2767169Z     >>> get_in(["purchase", "items", 0], transaction)
2025-03-14T04:33:16.2767523Z     'Apple'
2025-03-14T04:33:16.2767778Z     >>> get_in(["name"], transaction)
2025-03-14T04:33:16.2768095Z     'Alice'
2025-03-14T04:33:16.2768374Z     >>> get_in(["purchase", "total"], transaction)
2025-03-14T04:33:16.2768782Z     >>> get_in(["purchase", "items", "apple"], transaction)
2025-03-14T04:33:16.2769197Z     >>> get_in(["purchase", "items", 10], transaction)
2025-03-14T04:33:16.2769598Z     >>> get_in(["purchase", "total"], transaction, 0)
2025-03-14T04:33:16.2769952Z     0
2025-03-14T04:33:16.2770200Z     >>> get_in(["y"], {}, no_default=True)
2025-03-14T04:33:16.2770555Z     Traceback (most recent call last):
2025-03-14T04:33:16.2770875Z         ...
2025-03-14T04:33:16.2771110Z     KeyError: 'y'
2025-03-14T04:33:16.2771266Z 
2025-03-14T04:33:16.2771358Z     See Also:
2025-03-14T04:33:16.2771604Z         itertoolz.get
2025-03-14T04:33:16.2771879Z         operator.getitem
2025-03-14T04:33:16.2772149Z     
2025-03-14T04:33:16.2772531Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.2772912Z 
2025-03-14T04:33:16.2773591Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373.
2025-03-14T04:33:16.2774619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.2775121Z Group a collection by a key function
2025-03-14T04:33:16.2775351Z 
2025-03-14T04:33:16.2775522Z     >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2025-03-14T04:33:16.2775943Z     >>> groupby(len, names)  # doctest: +SKIP
2025-03-14T04:33:16.2776353Z     {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2025-03-14T04:33:16.2776648Z 
2025-03-14T04:33:16.2776762Z     >>> iseven = lambda x: x % 2 == 0
2025-03-14T04:33:16.2777158Z     >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2025-03-14T04:33:16.2777572Z     {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2025-03-14T04:33:16.2777813Z 
2025-03-14T04:33:16.2777963Z     Non-callable keys imply grouping on a member.
2025-03-14T04:33:16.2778235Z 
2025-03-14T04:33:16.2778330Z     >>> groupby(
2025-03-14T04:33:16.2778583Z     ...     "gender",
2025-03-14T04:33:16.2778838Z     ...     [
2025-03-14T04:33:16.2779106Z     ...         {"name": "Alice", "gender": "F"},
2025-03-14T04:33:16.2779468Z     ...         {"name": "Bob", "gender": "M"},
2025-03-14T04:33:16.2779831Z     ...         {"name": "Charlie", "gender": "M"},
2025-03-14T04:33:16.2780169Z     ...     ],
2025-03-14T04:33:16.2780419Z     ... )  # doctest:+SKIP
2025-03-14T04:33:16.2780712Z     {'F': [{'gender': 'F', 'name': 'Alice'}],
2025-03-14T04:33:16.2781065Z      'M': [{'gender': 'M', 'name': 'Bob'},
2025-03-14T04:33:16.2781484Z            {'gender': 'M', 'name': 'Charlie'}]}
2025-03-14T04:33:16.2781732Z 
2025-03-14T04:33:16.2781879Z     Not to be confused with ``itertools.groupby``
2025-03-14T04:33:16.2782149Z 
2025-03-14T04:33:16.2782244Z     See Also:
2025-03-14T04:33:16.2782488Z         countby
2025-03-14T04:33:16.2782729Z     
2025-03-14T04:33:16.2783120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.2783501Z 
2025-03-14T04:33:16.5947772Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601.
2025-03-14T04:33:16.5948943Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.5949521Z Applies Batch Normalization over a N-Dimensional input.
2025-03-14T04:33:16.5949832Z 
2025-03-14T04:33:16.5950181Z     The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2025-03-14T04:33:16.5950899Z     `Batch Normalization: Accelerating Deep Network Training by Reducing
2025-03-14T04:33:16.5951483Z     Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2025-03-14T04:33:16.5952021Z 
2025-03-14T04:33:16.5952141Z     .. math::
2025-03-14T04:33:16.5952295Z 
2025-03-14T04:33:16.5952524Z         y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2025-03-14T04:33:16.5952877Z 
2025-03-14T04:33:16.5953113Z     The mean and standard-deviation are calculated per-dimension over all
2025-03-14T04:33:16.5953709Z     mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2025-03-14T04:33:16.5954319Z     are learnable parameter vectors of size `C` (where `C` is the input size).
2025-03-14T04:33:16.5954878Z     By default, the elements of :math:`\gamma` are sampled from
2025-03-14T04:33:16.5955391Z     :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2025-03-14T04:33:16.5956000Z     The standard-deviation is calculated via the biased estimator, equivalent to
2025-03-14T04:33:16.5956514Z     `torch.var(input, unbiased=False)`.
2025-03-14T04:33:16.5956739Z 
2025-03-14T04:33:16.5956998Z     Also by default, during training this layer keeps running estimates of its
2025-03-14T04:33:16.5957602Z     computed mean and variance, which are then used for normalization during
2025-03-14T04:33:16.5958214Z     evaluation. The running estimates are kept with a default :attr:`momentum`
2025-03-14T04:33:16.5958742Z     of 0.1.
2025-03-14T04:33:16.5958876Z 
2025-03-14T04:33:16.5959200Z     If :attr:`track_running_stats` is set to ``False``, this layer then does not
2025-03-14T04:33:16.5959788Z     keep running estimates, and batch statistics are instead used during
2025-03-14T04:33:16.5960255Z     evaluation time as well.
2025-03-14T04:33:16.5960447Z 
2025-03-14T04:33:16.5960561Z     .. note::
2025-03-14T04:33:16.5960938Z         This :attr:`momentum` argument is different from one used in optimizer
2025-03-14T04:33:16.5961531Z         classes and the conventional notion of momentum. Mathematically, the
2025-03-14T04:33:16.5962029Z         update rule for running statistics here is
2025-03-14T04:33:16.5962547Z         :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2025-03-14T04:33:16.5963149Z         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2025-03-14T04:33:16.5963594Z         new observed value.
2025-03-14T04:33:16.5963792Z 
2025-03-14T04:33:16.5964099Z     Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2025-03-14T04:33:16.5964785Z     statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2025-03-14T04:33:16.5965350Z     Normalization or Spatio-temporal Batch Normalization.
2025-03-14T04:33:16.5965656Z 
2025-03-14T04:33:16.5965806Z     Currently :class:`SyncBatchNorm` only supports
2025-03-14T04:33:16.5966429Z     :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2025-03-14T04:33:16.5967150Z     :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2025-03-14T04:33:16.5967711Z     :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2025-03-14T04:33:16.5968155Z     Network with DDP.
2025-03-14T04:33:16.5968338Z 
2025-03-14T04:33:16.5968431Z     Args:
2025-03-14T04:33:16.5968741Z         num_features: :math:`C` from an expected input of size
2025-03-14T04:33:16.5969141Z             :math:`(N, C, +)`
2025-03-14T04:33:16.5969536Z         eps: a value added to the denominator for numerical stability.
2025-03-14T04:33:16.5969962Z             Default: ``1e-5``
2025-03-14T04:33:16.5970357Z         momentum: the value used for the running_mean and running_var
2025-03-14T04:33:16.5970893Z             computation. Can be set to ``None`` for cumulative moving average
2025-03-14T04:33:16.5971361Z             (i.e. simple average). Default: 0.1
2025-03-14T04:33:16.5971815Z         affine: a boolean value that when set to ``True``, this module has
2025-03-14T04:33:16.5972311Z             learnable affine parameters. Default: ``True``
2025-03-14T04:33:16.5972853Z         track_running_stats: a boolean value that when set to ``True``, this
2025-03-14T04:33:16.5973479Z             module tracks the running mean and variance, and when set to ``False``,
2025-03-14T04:33:16.5974073Z             this module does not track such statistics, and initializes statistics
2025-03-14T04:33:16.5974648Z             buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2025-03-14T04:33:16.5975216Z             When these buffers are ``None``, this module always uses batch statistics.
2025-03-14T04:33:16.5975738Z             in both training and eval modes. Default: ``True``
2025-03-14T04:33:16.5976272Z         process_group: synchronization of stats happen within each process group
2025-03-14T04:33:16.5976878Z             individually. Default behavior is synchronization across the whole
2025-03-14T04:33:16.5977330Z             world
2025-03-14T04:33:16.5977482Z 
2025-03-14T04:33:16.5977593Z     Shape:
2025-03-14T04:33:16.5977845Z         - Input: :math:`(N, C, +)`
2025-03-14T04:33:16.5978217Z         - Output: :math:`(N, C, +)` (same shape as input)
2025-03-14T04:33:16.5978478Z 
2025-03-14T04:33:16.5978594Z     .. note::
2025-03-14T04:33:16.5978996Z         Synchronization of batchnorm statistics occurs only while training, i.e.
2025-03-14T04:33:16.5979563Z         synchronization is disabled when ``model.eval()`` is set or if
2025-03-14T04:33:16.5980066Z         ``self.training`` is otherwise ``False``.
2025-03-14T04:33:16.5980323Z 
2025-03-14T04:33:16.5980422Z     Examples::
2025-03-14T04:33:16.5980582Z 
2025-03-14T04:33:16.5980689Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:16.5981013Z         >>> # With Learnable Parameters
2025-03-14T04:33:16.5981364Z         >>> m = nn.SyncBatchNorm(100)
2025-03-14T04:33:16.5981717Z         >>> # creating process group (optional)
2025-03-14T04:33:16.5982118Z         >>> # ranks is a list of int identifying rank ids.
2025-03-14T04:33:16.5982497Z         >>> ranks = list(range(8))
2025-03-14T04:33:16.5982820Z         >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-14T04:33:16.5983203Z         >>> # Note: every rank calls into new_group for every
2025-03-14T04:33:16.5983636Z         >>> # process group created, even if that rank is not
2025-03-14T04:33:16.5984026Z         >>> # part of the group.
2025-03-14T04:33:16.5984490Z         >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-14T04:33:16.5985080Z         >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-14T04:33:16.5985540Z         >>> # Without Learnable Parameters
2025-03-14T04:33:16.5985996Z         >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2025-03-14T04:33:16.5986463Z         >>> input = torch.randn(20, 100, 35, 45, 10)
2025-03-14T04:33:16.5986822Z         >>> output = m(input)
2025-03-14T04:33:16.5987011Z 
2025-03-14T04:33:16.5987187Z         >>> # network is nn.BatchNorm layer
2025-03-14T04:33:16.5987733Z         >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2025-03-14T04:33:16.5988318Z         >>> # only single gpu per process is currently supported
2025-03-14T04:33:16.5988827Z         >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2025-03-14T04:33:16.5989275Z         >>>                         sync_bn_network,
2025-03-14T04:33:16.5989655Z         >>>                         device_ids=[args.local_rank],
2025-03-14T04:33:16.5990046Z         >>>                         output_device=args.local_rank)
2025-03-14T04:33:16.5990395Z     
2025-03-14T04:33:16.5990783Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.5991166Z 
2025-03-14T04:33:16.5991817Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=825.
2025-03-14T04:33:16.5992847Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.5993544Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2025-03-14T04:33:16.5994015Z 
2025-03-14T04:33:16.5994111Z         Args:
2025-03-14T04:33:16.5994505Z             module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2025-03-14T04:33:16.5995105Z             process_group (optional): process group to scope synchronization,
2025-03-14T04:33:16.5995575Z                 default is the whole world
2025-03-14T04:33:16.5995809Z 
2025-03-14T04:33:16.5995903Z         Returns:
2025-03-14T04:33:16.5996316Z             The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2025-03-14T04:33:16.5996922Z             layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2025-03-14T04:33:16.5997489Z             a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2025-03-14T04:33:16.5997924Z             instead.
2025-03-14T04:33:16.5998094Z 
2025-03-14T04:33:16.5998198Z         Example::
2025-03-14T04:33:16.5998344Z 
2025-03-14T04:33:16.5998486Z             >>> # Network with nn.BatchNorm layer
2025-03-14T04:33:16.5998884Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:16.5999367Z             >>> module = torch.nn.Sequential(
2025-03-14T04:33:16.5999738Z             >>>            torch.nn.Linear(20, 100),
2025-03-14T04:33:16.6000159Z             >>>            torch.nn.BatchNorm1d(100),
2025-03-14T04:33:16.6000514Z             >>>          ).cuda()
2025-03-14T04:33:16.6000851Z             >>> # creating process group (optional)
2025-03-14T04:33:16.6001257Z             >>> # ranks is a list of int identifying rank ids.
2025-03-14T04:33:16.6001641Z             >>> ranks = list(range(8))
2025-03-14T04:33:16.6001984Z             >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-14T04:33:16.6002358Z             >>> # Note: every rank calls into new_group for every
2025-03-14T04:33:16.6002796Z             >>> # process group created, even if that rank is not
2025-03-14T04:33:16.6003188Z             >>> # part of the group.
2025-03-14T04:33:16.6003538Z             >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:16.6004039Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-14T04:33:16.6004614Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-14T04:33:16.6005242Z             >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2025-03-14T04:33:16.6005671Z 
2025-03-14T04:33:16.6005761Z         
2025-03-14T04:33:16.6006156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.6006540Z 
2025-03-14T04:33:16.6178980Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60.
2025-03-14T04:33:16.6180144Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.6180601Z 
2025-03-14T04:33:16.6180912Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2025-03-14T04:33:16.6181356Z 
2025-03-14T04:33:16.6181631Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2025-03-14T04:33:16.6182262Z   be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2025-03-14T04:33:16.6182622Z 
2025-03-14T04:33:16.6182939Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2025-03-14T04:33:16.6183644Z   a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2025-03-14T04:33:16.6184197Z   (tuple of `(name, size)` tuples) for `NamedTensor` input.
2025-03-14T04:33:16.6184499Z 
2025-03-14T04:33:16.6184590Z Shape:
2025-03-14T04:33:16.6184949Z     - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2025-03-14T04:33:16.6185549Z       dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2025-03-14T04:33:16.6186146Z     - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2025-03-14T04:33:16.6186678Z       :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2025-03-14T04:33:16.6186936Z 
2025-03-14T04:33:16.6187026Z Args:
2025-03-14T04:33:16.6187317Z     dim (Union[int, str]): Dimension to be unflattened
2025-03-14T04:33:16.6187946Z     unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2025-03-14T04:33:16.6188427Z 
2025-03-14T04:33:16.6188523Z Examples:
2025-03-14T04:33:16.6188782Z     >>> input = torch.randn(2, 50)
2025-03-14T04:33:16.6189114Z     >>> # With tuple of ints
2025-03-14T04:33:16.6189422Z     >>> m = nn.Sequential(
2025-03-14T04:33:16.6189732Z     >>>     nn.Linear(50, 50),
2025-03-14T04:33:16.6190049Z     >>>     nn.Unflatten(1, (2, 5, 5))
2025-03-14T04:33:16.6190367Z     >>> )
2025-03-14T04:33:16.6190613Z     >>> output = m(input)
2025-03-14T04:33:16.6190902Z     >>> output.size()
2025-03-14T04:33:16.6191167Z     torch.Size([2, 2, 5, 5])
2025-03-14T04:33:16.6191467Z     >>> # With torch.Size
2025-03-14T04:33:16.6191755Z     >>> m = nn.Sequential(
2025-03-14T04:33:16.6192045Z     >>>     nn.Linear(50, 50),
2025-03-14T04:33:16.6192374Z     >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2025-03-14T04:33:16.6192714Z     >>> )
2025-03-14T04:33:16.6192961Z     >>> output = m(input)
2025-03-14T04:33:16.6193296Z     >>> output.size()
2025-03-14T04:33:16.6193573Z     torch.Size([2, 2, 5, 5])
2025-03-14T04:33:16.6193901Z     >>> # With namedshape (tuple of tuples)
2025-03-14T04:33:16.6194301Z     >>> input = torch.randn(2, 50, names=('N', 'features'))
2025-03-14T04:33:16.6194789Z     >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
2025-03-14T04:33:16.6195235Z     >>> output = unflatten(input)
2025-03-14T04:33:16.6195547Z     >>> output.size()
2025-03-14T04:33:16.6195819Z     torch.Size([2, 2, 5, 5])
2025-03-14T04:33:16.6196016Z 
2025-03-14T04:33:16.6196289Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.6196662Z 
2025-03-14T04:33:16.6506275Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1700.
2025-03-14T04:33:16.6507288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.6507888Z Creates a criterion that measures the triplet loss given input
2025-03-14T04:33:16.6508423Z     tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2025-03-14T04:33:16.6508965Z     positive, and negative examples, respectively), and a nonnegative,
2025-03-14T04:33:16.6509565Z     real-valued function ("distance function") used to compute the relationship
2025-03-14T04:33:16.6510174Z     between the anchor and positive example ("positive distance") and the
2025-03-14T04:33:16.6510795Z     anchor and negative example ("negative distance").
2025-03-14T04:33:16.6511112Z 
2025-03-14T04:33:16.6511343Z     The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2025-03-14T04:33:16.6511795Z     can be described as:
2025-03-14T04:33:16.6511972Z 
2025-03-14T04:33:16.6512106Z     .. math::
2025-03-14T04:33:16.6512408Z         \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2025-03-14T04:33:16.6512839Z         l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2025-03-14T04:33:16.6513111Z 
2025-03-14T04:33:16.6513378Z     where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2025-03-14T04:33:16.6514047Z     quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2025-03-14T04:33:16.6514715Z     and :math:`margin` is a nonnegative margin representing the minimum difference
2025-03-14T04:33:16.6515333Z     between the positive and negative distances that is required for the loss to
2025-03-14T04:33:16.6515966Z     be 0.  The input tensors have :math:`N` elements each and can be of any shape
2025-03-14T04:33:16.6516447Z     that the distance function can handle.
2025-03-14T04:33:16.6516675Z 
2025-03-14T04:33:16.6516863Z     If :attr:`reduction` is not ``'none'``
2025-03-14T04:33:16.6517209Z     (default ``'mean'``), then:
2025-03-14T04:33:16.6517398Z 
2025-03-14T04:33:16.6517507Z     .. math::
2025-03-14T04:33:16.6517748Z         \ell(x, y) =
2025-03-14T04:33:16.6517996Z         \begin{cases}
2025-03-14T04:33:16.6518376Z             \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2025-03-14T04:33:16.6518900Z             \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2025-03-14T04:33:16.6519370Z         \end{cases}
2025-03-14T04:33:16.6519535Z 
2025-03-14T04:33:16.6519773Z     See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2025-03-14T04:33:16.6520390Z     loss for input tensors using the :math:`l_p` distance as the distance function.
2025-03-14T04:33:16.6520774Z 
2025-03-14T04:33:16.6520866Z     Args:
2025-03-14T04:33:16.6521286Z         distance_function (Callable, optional): A nonnegative, real-valued function that
2025-03-14T04:33:16.6521891Z             quantifies the closeness of two tensors. If not specified,
2025-03-14T04:33:16.6522391Z             `nn.PairwiseDistance` will be used.  Default: ``None``
2025-03-14T04:33:16.6522960Z         margin (float, optional): A nonnegative margin representing the minimum difference
2025-03-14T04:33:16.6523679Z             between the positive and negative distances required for the loss to be 0. Larger
2025-03-14T04:33:16.6524362Z             margins penalize cases where the negative examples are not distant enough from the
2025-03-14T04:33:16.6524946Z             anchors, relative to the positives. Default: :math:`1`.
2025-03-14T04:33:16.6525502Z         swap (bool, optional): Whether to use the distance swap described in the paper
2025-03-14T04:33:16.6526144Z             `Learning shallow convolutional feature descriptors with triplet losses` by
2025-03-14T04:33:16.6526784Z             V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2025-03-14T04:33:16.6527423Z             negative example than the anchor is, swaps the positive example and the anchor in
2025-03-14T04:33:16.6527965Z             the loss computation. Default: ``False``.
2025-03-14T04:33:16.6528504Z         reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2025-03-14T04:33:16.6529104Z             ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-03-14T04:33:16.6529606Z             ``'mean'``: the sum of the output will be divided by the number of
2025-03-14T04:33:16.6530161Z             elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2025-03-14T04:33:16.6530518Z 
2025-03-14T04:33:16.6530522Z 
2025-03-14T04:33:16.6530629Z     Shape:
2025-03-14T04:33:16.6531017Z         - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2025-03-14T04:33:16.6531621Z           as supported by the distance function.
2025-03-14T04:33:16.6532133Z         - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2025-03-14T04:33:16.6532618Z           otherwise.
2025-03-14T04:33:16.6532774Z 
2025-03-14T04:33:16.6532891Z     Examples::
2025-03-14T04:33:16.6533031Z 
2025-03-14T04:33:16.6533155Z     >>> # Initialize embeddings
2025-03-14T04:33:16.6533482Z     >>> embedding = nn.Embedding(1000, 128)
2025-03-14T04:33:16.6533851Z     >>> anchor_ids = torch.randint(0, 1000, (1,))
2025-03-14T04:33:16.6534234Z     >>> positive_ids = torch.randint(0, 1000, (1,))
2025-03-14T04:33:16.6534609Z     >>> negative_ids = torch.randint(0, 1000, (1,))
2025-03-14T04:33:16.6534985Z     >>> anchor = embedding(anchor_ids)
2025-03-14T04:33:16.6535344Z     >>> positive = embedding(positive_ids)
2025-03-14T04:33:16.6535714Z     >>> negative = embedding(negative_ids)
2025-03-14T04:33:16.6536039Z     >>>
2025-03-14T04:33:16.6536285Z     >>> # Built-in Distance Function
2025-03-14T04:33:16.6536611Z     >>> triplet_loss = \
2025-03-14T04:33:16.6537074Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2025-03-14T04:33:16.6537674Z     >>> output = triplet_loss(anchor, positive, negative)
2025-03-14T04:33:16.6538056Z     >>> output.backward()
2025-03-14T04:33:16.6538332Z     >>>
2025-03-14T04:33:16.6538578Z     >>> # Custom Distance Function
2025-03-14T04:33:16.6538903Z     >>> def l_infinity(x1, x2):
2025-03-14T04:33:16.6539270Z     >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2025-03-14T04:33:16.6539641Z     >>>
2025-03-14T04:33:16.6539967Z     >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2025-03-14T04:33:16.6540390Z     >>> triplet_loss = (
2025-03-14T04:33:16.6540846Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2025-03-14T04:33:16.6541414Z     >>> output = triplet_loss(anchor, positive, negative)
2025-03-14T04:33:16.6541803Z     >>> output.backward()
2025-03-14T04:33:16.6542088Z     >>>
2025-03-14T04:33:16.6542353Z     >>> # Custom Distance Function (Lambda)
2025-03-14T04:33:16.6542690Z     >>> triplet_loss = (
2025-03-14T04:33:16.6543009Z     >>>     nn.TripletMarginWithDistanceLoss(
2025-03-14T04:33:16.6543490Z     >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2025-03-14T04:33:16.6543992Z     >>> output = triplet_loss(anchor, positive, negative)
2025-03-14T04:33:16.6544377Z     >>> output.backward()
2025-03-14T04:33:16.6544586Z 
2025-03-14T04:33:16.6544700Z     Reference:
2025-03-14T04:33:16.6545162Z         V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2025-03-14T04:33:16.6545827Z         https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html
2025-03-14T04:33:16.6546271Z     
2025-03-14T04:33:16.6546663Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2025-03-14T04:33:16.6547040Z 
2025-03-14T04:33:16.7068226Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395.
2025-03-14T04:33:16.7069187Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.7069745Z Computes a partial inverse of :class:`MaxPool2d`.
2025-03-14T04:33:16.7070026Z 
2025-03-14T04:33:16.7070293Z     :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2025-03-14T04:33:16.7070691Z 
2025-03-14T04:33:16.7070929Z     :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2025-03-14T04:33:16.7071538Z     including the indices of the maximal values and computes a partial inverse
2025-03-14T04:33:16.7072061Z     in which all non-maximal values are set to zero.
2025-03-14T04:33:16.7072339Z 
2025-03-14T04:33:16.7072433Z     Note:
2025-03-14T04:33:16.7072892Z         This operation may behave nondeterministically when the input indices has repeat values.
2025-03-14T04:33:16.7073878Z         See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2025-03-14T04:33:16.7074421Z 
2025-03-14T04:33:16.7074689Z     .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2025-03-14T04:33:16.7075233Z               sizes. Hence, the inversion process can get ambiguous.
2025-03-14T04:33:16.7075732Z               To accommodate this, you can provide the needed output size
2025-03-14T04:33:16.7076270Z               as an additional argument :attr:`output_size` in the forward call.
2025-03-14T04:33:16.7076738Z               See the Inputs and Example below.
2025-03-14T04:33:16.7076973Z 
2025-03-14T04:33:16.7077079Z     Args:
2025-03-14T04:33:16.7077408Z         kernel_size (int or tuple): Size of the max pooling window.
2025-03-14T04:33:16.7077894Z         stride (int or tuple): Stride of the max pooling window.
2025-03-14T04:33:16.7078329Z             It is set to :attr:`kernel_size` by default.
2025-03-14T04:33:16.7078781Z         padding (int or tuple): Padding that was added to the input
2025-03-14T04:33:16.7079080Z 
2025-03-14T04:33:16.7079245Z     Inputs:
2025-03-14T04:33:16.7079517Z         - `input`: the input Tensor to invert
2025-03-14T04:33:16.7080029Z         - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2025-03-14T04:33:16.7080511Z         - `output_size` (optional): the targeted output size
2025-03-14T04:33:16.7080797Z 
2025-03-14T04:33:16.7080890Z     Shape:
2025-03-14T04:33:16.7081222Z         - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2025-03-14T04:33:16.7081752Z         - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2025-03-14T04:33:16.7082099Z 
2025-03-14T04:33:16.7082198Z           .. math::
2025-03-14T04:33:16.7082630Z             H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2025-03-14T04:33:16.7083027Z 
2025-03-14T04:33:16.7083123Z           .. math::
2025-03-14T04:33:16.7083548Z             W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2025-03-14T04:33:16.7083940Z 
2025-03-14T04:33:16.7084110Z           or as given by :attr:`output_size` in the call operator
2025-03-14T04:33:16.7084403Z 
2025-03-14T04:33:16.7084502Z     Example::
2025-03-14T04:33:16.7084654Z 
2025-03-14T04:33:16.7084823Z         >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2025-03-14T04:33:16.7085241Z         >>> unpool = nn.MaxUnpool2d(2, stride=2)
2025-03-14T04:33:16.7085674Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2025-03-14T04:33:16.7086051Z                                     [ 5.,  6.,  7.,  8.],
2025-03-14T04:33:16.7086401Z                                     [ 9., 10., 11., 12.],
2025-03-14T04:33:16.7086753Z                                     [13., 14., 15., 16.]]]])
2025-03-14T04:33:16.7087113Z         >>> output, indices = pool(input)
2025-03-14T04:33:16.7087465Z         >>> unpool(output, indices)
2025-03-14T04:33:16.7087804Z         tensor([[[[  0.,   0.,   0.,   0.],
2025-03-14T04:33:16.7088139Z                   [  0.,   6.,   0.,   8.],
2025-03-14T04:33:16.7088461Z                   [  0.,   0.,   0.,   0.],
2025-03-14T04:33:16.7088789Z                   [  0.,  14.,   0.,  16.]]]])
2025-03-14T04:33:16.7089226Z         >>> # Now using output_size to resolve an ambiguous size for the inverse
2025-03-14T04:33:16.7089715Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2025-03-14T04:33:16.7090100Z                                     [ 6.,  7.,  8.,  9., 10.],
2025-03-14T04:33:16.7090461Z                                     [11., 12., 13., 14., 15.],
2025-03-14T04:33:16.7090824Z                                     [16., 17., 18., 19., 20.]]]])
2025-03-14T04:33:16.7091172Z         >>> output, indices = pool(input)
2025-03-14T04:33:16.7091584Z         >>> # This call will not work without specifying output_size
2025-03-14T04:33:16.7092048Z         >>> unpool(output, indices, output_size=input.size())
2025-03-14T04:33:16.7092504Z         tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2025-03-14T04:33:16.7092838Z                   [ 0.,  7.,  0.,  9.,  0.],
2025-03-14T04:33:16.7093171Z                   [ 0.,  0.,  0.,  0.,  0.],
2025-03-14T04:33:16.7093503Z                   [ 0., 17.,  0., 19.,  0.]]]])
2025-03-14T04:33:16.7093738Z 
2025-03-14T04:33:16.7093742Z 
2025-03-14T04:33:16.7093832Z     
2025-03-14T04:33:16.7094218Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.7094601Z 
2025-03-14T04:33:16.7341216Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270.
2025-03-14T04:33:16.7342308Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.7343012Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2025-03-14T04:33:16.7343458Z 
2025-03-14T04:33:16.7343790Z     For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2025-03-14T04:33:16.7344354Z     and with 2D inputs, this class
2025-03-14T04:33:16.7344576Z 
2025-03-14T04:33:16.7344990Z         * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2025-03-14T04:33:16.7345734Z         * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2025-03-14T04:33:16.7346478Z         * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2025-03-14T04:33:16.7346914Z 
2025-03-14T04:33:16.7347268Z     However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2025-03-14T04:33:16.7347847Z     operations.
2025-03-14T04:33:16.7348008Z 
2025-03-14T04:33:16.7348271Z     EmbeddingBag also supports per-sample weights as an argument to the forward
2025-03-14T04:33:16.7349040Z     pass. This scales the output of the Embedding before performing a weighted
2025-03-14T04:33:16.7349696Z     reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2025-03-14T04:33:16.7350322Z     only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2025-03-14T04:33:16.7350808Z     :attr:`per_sample_weights`.
2025-03-14T04:33:16.7351023Z 
2025-03-14T04:33:16.7351115Z     Args:
2025-03-14T04:33:16.7351444Z         num_embeddings (int): size of the dictionary of embeddings
2025-03-14T04:33:16.7351979Z         embedding_dim (int): the size of each embedding vector
2025-03-14T04:33:16.7352586Z         max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2025-03-14T04:33:16.7353193Z                                     is renormalized to have norm :attr:`max_norm`.
2025-03-14T04:33:16.7353836Z         norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2025-03-14T04:33:16.7354650Z         scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2025-03-14T04:33:16.7355284Z                                                 the words in the mini-batch. Default ``False``.
2025-03-14T04:33:16.7355779Z                                                 Note: this option is not supported when ``mode="max"``.
2025-03-14T04:33:16.7356354Z         mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2025-03-14T04:33:16.7356966Z                                  ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2025-03-14T04:33:16.7357546Z                                  into consideration. ``"mean"`` computes the average of the values
2025-03-14T04:33:16.7358084Z                                  in the bag, ``"max"`` computes the max value over each bag.
2025-03-14T04:33:16.7358517Z                                  Default: ``"mean"``
2025-03-14T04:33:16.7359187Z         sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2025-03-14T04:33:16.7359953Z                                  Notes for more details regarding sparse gradients. Note: this option is not
2025-03-14T04:33:16.7360484Z                                  supported when ``mode="max"``.
2025-03-14T04:33:16.7361119Z         include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2025-03-14T04:33:16.7361857Z                                       is equivalent to the size of `indices`. This matches the CSR format.
2025-03-14T04:33:16.7362555Z         padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2025-03-14T04:33:16.7363299Z                                      gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2025-03-14T04:33:16.7363960Z                                      during training, i.e. it remains as a fixed "pad". For a newly constructed
2025-03-14T04:33:16.7364622Z                                      EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2025-03-14T04:33:16.7365279Z                                      zeros, but can be updated to another value to be used as the padding vector.
2025-03-14T04:33:16.7366364Z                                      Note that the embedding vector at :attr:`padding_idx` is excluded from the
2025-03-14T04:33:16.7366862Z                                      reduction.
2025-03-14T04:33:16.7367088Z 
2025-03-14T04:33:16.7367206Z     Attributes:
2025-03-14T04:33:16.7367688Z         weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2025-03-14T04:33:16.7368281Z                          initialized from :math:`\mathcal{N}(0, 1)`.
2025-03-14T04:33:16.7368542Z 
2025-03-14T04:33:16.7368669Z     Examples::
2025-03-14T04:33:16.7368811Z 
2025-03-14T04:33:16.7369001Z         >>> # an EmbeddingBag module containing 10 tensors of size 3
2025-03-14T04:33:16.7369469Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2025-03-14T04:33:16.7369890Z         >>> # a batch of 2 samples of 4 indices each
2025-03-14T04:33:16.7370340Z         >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2025-03-14T04:33:16.7370816Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-14T04:33:16.7371223Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:16.7371608Z         >>> embedding_sum(input, offsets)
2025-03-14T04:33:16.7372024Z         tensor([[-0.8861, -5.4350, -0.0523],
2025-03-14T04:33:16.7372366Z                 [ 1.1306, -2.5798, -1.0044]])
2025-03-14T04:33:16.7372597Z 
2025-03-14T04:33:16.7372715Z         >>> # Example with padding_idx
2025-03-14T04:33:16.7373157Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2025-03-14T04:33:16.7373688Z         >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2025-03-14T04:33:16.7374162Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-14T04:33:16.7374555Z         >>> embedding_sum(input, offsets)
2025-03-14T04:33:16.7374900Z         tensor([[ 0.0000,  0.0000,  0.0000],
2025-03-14T04:33:16.7375234Z                 [-0.7082,  3.2145, -2.6251]])
2025-03-14T04:33:16.7375461Z 
2025-03-14T04:33:16.7375641Z         >>> # An EmbeddingBag can be loaded from an Embedding like so
2025-03-14T04:33:16.7376098Z         >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2025-03-14T04:33:16.7376534Z         >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2025-03-14T04:33:16.7376926Z                 embedding.weight,
2025-03-14T04:33:16.7377272Z                 padding_idx=embedding.padding_idx,
2025-03-14T04:33:16.7377625Z                 mode='sum')
2025-03-14T04:33:16.7377902Z     
2025-03-14T04:33:16.7378288Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.7378659Z 
2025-03-14T04:33:16.7673363Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1742.
2025-03-14T04:33:16.7674483Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.7674874Z 
2025-03-14T04:33:16.7675130Z Context manager for training with uneven inputs across processes in DDP.
2025-03-14T04:33:16.7675482Z 
2025-03-14T04:33:16.7675727Z This context manager will keep track of already-joined DDP processes,
2025-03-14T04:33:16.7676295Z and "shadow" the forward and backward passes by inserting collective
2025-03-14T04:33:16.7676859Z communication operations to match with the ones created by non-joined
2025-03-14T04:33:16.7677450Z DDP processes. This will ensure each collective call has a corresponding
2025-03-14T04:33:16.7678066Z call by already-joined DDP processes, preventing hangs or errors that
2025-03-14T04:33:16.7678619Z would otherwise happen when training with uneven inputs across
2025-03-14T04:33:16.7679232Z processes. Alternatively, if the flag ``throw_on_early_termination`` is
2025-03-14T04:33:16.7679818Z specified to be ``True``, all trainers will throw an error once one rank
2025-03-14T04:33:16.7680356Z runs out of inputs, allowing these errors to be caught and handled
2025-03-14T04:33:16.7680854Z according to application logic.
2025-03-14T04:33:16.7681057Z 
2025-03-14T04:33:16.7681294Z Once all DDP processes have joined, the context manager will broadcast
2025-03-14T04:33:16.7681870Z the model corresponding to the last joined process to all processes to
2025-03-14T04:33:16.7682373Z ensure the model is the same across all processes
2025-03-14T04:33:16.7682759Z (which is guaranteed by DDP).
2025-03-14T04:33:16.7682957Z 
2025-03-14T04:33:16.7683183Z To use this to enable training with uneven inputs across processes,
2025-03-14T04:33:16.7683736Z simply wrap this context manager around your training loop. No further
2025-03-14T04:33:16.7684263Z modifications to the model or data loading is required.
2025-03-14T04:33:16.7684547Z 
2025-03-14T04:33:16.7684678Z .. warning::
2025-03-14T04:33:16.7685025Z     If the model or training loop this context manager is wrapped around
2025-03-14T04:33:16.7685549Z     has additional distributed collective operations, such as
2025-03-14T04:33:16.7686058Z     ``SyncBatchNorm`` in the model's forward pass, then the flag
2025-03-14T04:33:16.7686581Z     ``throw_on_early_termination`` must be enabled. This is because this
2025-03-14T04:33:16.7687131Z     context manager is not aware of non-DDP collective communication.
2025-03-14T04:33:16.7687692Z     This flag will cause all ranks to throw when any one rank
2025-03-14T04:33:16.7688207Z     exhausts inputs, allowing these errors to be caught and recovered
2025-03-14T04:33:16.7688654Z     from across all ranks.
2025-03-14T04:33:16.7688851Z 
2025-03-14T04:33:16.7688943Z Args:
2025-03-14T04:33:16.7689269Z     divide_by_initial_world_size (bool): If ``True``, will divide
2025-03-14T04:33:16.7689798Z         gradients by the initial ``world_size`` DDP training was launched
2025-03-14T04:33:16.7690314Z         with. If ``False``, will compute the effective world size
2025-03-14T04:33:16.7690809Z         (number of ranks that have not depleted their inputs yet) and
2025-03-14T04:33:16.7691283Z         divide gradients by that during allreduce. Set
2025-03-14T04:33:16.7691739Z         ``divide_by_initial_world_size=True`` to ensure every input
2025-03-14T04:33:16.7692264Z         sample including the uneven inputs have equal weight in terms of
2025-03-14T04:33:16.7692787Z         how much they contribute to the global gradient. This is
2025-03-14T04:33:16.7693268Z         achieved by always dividing the gradient by the initial
2025-03-14T04:33:16.7693766Z         ``world_size`` even when we encounter uneven inputs. If you set
2025-03-14T04:33:16.7694256Z         this to ``False``, we divide the gradient by the remaining
2025-03-14T04:33:16.7694759Z         number of nodes. This ensures parity with training on a smaller
2025-03-14T04:33:16.7695273Z         ``world_size`` although it also means the uneven inputs would
2025-03-14T04:33:16.7695861Z         contribute more towards the global gradient. Typically, you
2025-03-14T04:33:16.7696369Z         would want to set this to ``True`` for cases where the last few
2025-03-14T04:33:16.7696890Z         inputs of your training job are uneven. In extreme cases, where
2025-03-14T04:33:16.7697407Z         there is a large discrepancy in the number of inputs, setting
2025-03-14T04:33:16.7697853Z         this to ``False`` might provide better results.
2025-03-14T04:33:16.7698339Z     enable (bool): Whether to enable uneven input detection or not. Pass
2025-03-14T04:33:16.7698858Z         in ``enable=False`` to disable in cases where you know that
2025-03-14T04:33:16.7699345Z         inputs are even across participating processes. Default is
2025-03-14T04:33:16.7699806Z         ``True``.
2025-03-14T04:33:16.7700193Z     throw_on_early_termination (bool): Whether to throw an error
2025-03-14T04:33:16.7700683Z         or continue training when at least one rank has exhausted
2025-03-14T04:33:16.7701181Z         inputs. If ``True``, will throw upon the first rank reaching end
2025-03-14T04:33:16.7701672Z         of data. If ``False``, will continue training with a smaller
2025-03-14T04:33:16.7702207Z         effective world size until all ranks are joined. Note that if
2025-03-14T04:33:16.7702656Z         this flag is specified, then the flag
2025-03-14T04:33:16.7703080Z         ``divide_by_initial_world_size`` would be ignored. Default
2025-03-14T04:33:16.7703480Z         is ``False``.
2025-03-14T04:33:16.7703642Z 
2025-03-14T04:33:16.7703663Z 
2025-03-14T04:33:16.7703764Z Example::
2025-03-14T04:33:16.7703897Z 
2025-03-14T04:33:16.7704035Z     >>> # xdoctest: +SKIP("Distributed")
2025-03-14T04:33:16.7704371Z     >>> import torch
2025-03-14T04:33:16.7704662Z     >>> import torch.distributed as dist
2025-03-14T04:33:16.7704988Z     >>> import os
2025-03-14T04:33:16.7705278Z     >>> import torch.multiprocessing as mp
2025-03-14T04:33:16.7705633Z     >>> import torch.nn as nn
2025-03-14T04:33:16.7705948Z     >>> # On each spawned worker
2025-03-14T04:33:16.7706247Z     >>> def worker(rank):
2025-03-14T04:33:16.7706609Z     >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2025-03-14T04:33:16.7707033Z     >>>     torch.cuda.set_device(rank)
2025-03-14T04:33:16.7707404Z     >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2025-03-14T04:33:16.7707839Z     >>>     model = torch.nn.parallel.DistributedDataParallel(
2025-03-14T04:33:16.7708328Z     >>>         model, device_ids=[rank], output_device=rank
2025-03-14T04:33:16.7708686Z     >>>     )
2025-03-14T04:33:16.7708965Z     >>>     # Rank 1 gets one more input than rank 0.
2025-03-14T04:33:16.7709409Z     >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2025-03-14T04:33:16.7709842Z     >>>     with model.join():
2025-03-14T04:33:16.7710151Z     >>>         for _ in range(5):
2025-03-14T04:33:16.7710470Z     >>>             for inp in inputs:
2025-03-14T04:33:16.7710809Z     >>>                 loss = model(inp).sum()
2025-03-14T04:33:16.7711160Z     >>>                 loss.backward()
2025-03-14T04:33:16.7711575Z     >>>     # Without the join() API, the below synchronization will hang
2025-03-14T04:33:16.7712043Z     >>>     # blocking for rank 1's allreduce to complete.
2025-03-14T04:33:16.7712440Z     >>>     torch.cuda.synchronize(device=rank)
2025-03-14T04:33:16.7712679Z 
2025-03-14T04:33:16.7712951Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.7713317Z 
2025-03-14T04:33:16.7714080Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2033.
2025-03-14T04:33:16.7715172Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.7715551Z 
2025-03-14T04:33:16.7715879Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2025-03-14T04:33:16.7716308Z 
2025-03-14T04:33:16.7716609Z Registers an optimizer with DDP such that the optimization for a
2025-03-14T04:33:16.7717157Z parameter will run immediately when that parameter's gradient is
2025-03-14T04:33:16.7717698Z finished with reduction, instead of waiting for all parameters'
2025-03-14T04:33:16.7718247Z gradients to finish reduction. This can result in a training speedup
2025-03-14T04:33:16.7718814Z depending on your workload since the optimizer can run while gradient
2025-03-14T04:33:16.7719469Z reduction for other parameters are still ongoing. In addition, this has
2025-03-14T04:33:16.7720052Z the potential to reduce peak memory consumption during training, as it
2025-03-14T04:33:16.7720608Z only needs to load the per-parameter optimizer states of a single
2025-03-14T04:33:16.7721151Z parameter at a time, instead of loading all per-parameter optimizer
2025-03-14T04:33:16.7721583Z states at once.
2025-03-14T04:33:16.7721729Z 
2025-03-14T04:33:16.7721834Z Args:
2025-03-14T04:33:16.7722155Z     optim (Type): a ``torch.optim.Optimizer`` class to be registered
2025-03-14T04:33:16.7722591Z     as a fused optimizer.
2025-03-14T04:33:16.7722949Z     *args (Sequence[Any]): Arguments to forward to `optim`.
2025-03-14T04:33:16.7723497Z     optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2025-03-14T04:33:16.7724067Z     to optimize, similar to `params` argument of traditional `torch.optim`
2025-03-14T04:33:16.7724634Z     Optimizers. If this is omitted, all DDP model parameters will be
2025-03-14T04:33:16.7725065Z     optimized.
2025-03-14T04:33:16.7725420Z     **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2025-03-14T04:33:16.7725750Z 
2025-03-14T04:33:16.7725848Z .. warning ::
2025-03-14T04:33:16.7726211Z     _register_fused_optim should only be called once on a DDP instance,
2025-03-14T04:33:16.7726768Z     and registering multiple fused optimizers for the same DDP model
2025-03-14T04:33:16.7727238Z     is not currently supported. Please ping
2025-03-14T04:33:16.7727738Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-14T04:33:16.7728200Z     for your use case.
2025-03-14T04:33:16.7728384Z 
2025-03-14T04:33:16.7728485Z .. warning ::
2025-03-14T04:33:16.7728836Z     _register_fused_optim and register_comm_hook currently do not
2025-03-14T04:33:16.7729376Z     compose together, meaning that custom DDP communication hooks are
2025-03-14T04:33:16.7729899Z     not supported with overlapped optimizers. Please ping
2025-03-14T04:33:16.7730466Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-14T04:33:16.7730926Z     for your use case.
2025-03-14T04:33:16.7731108Z 
2025-03-14T04:33:16.7731209Z .. warning ::
2025-03-14T04:33:16.7731596Z     Gradient accumulation and DDP `no_sync` are currently not supported
2025-03-14T04:33:16.7732083Z     with overlapped optimizer. Please ping
2025-03-14T04:33:16.7732561Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-14T04:33:16.7733023Z     for your use case.
2025-03-14T04:33:16.7733193Z 
2025-03-14T04:33:16.7733307Z Example::
2025-03-14T04:33:16.7733442Z 
2025-03-14T04:33:16.7733599Z     >>> # xdoctest: +SKIP("No rendezvous handler")
2025-03-14T04:33:16.7734164Z     >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2025-03-14T04:33:16.7734800Z     >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2025-03-14T04:33:16.7735220Z     >>> lr = 1e-2
2025-03-14T04:33:16.7735482Z     >>> betas = (0.9, 0.99)
2025-03-14T04:33:16.7735774Z     >>> eps = 1e-6
2025-03-14T04:33:16.7736167Z     >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2025-03-14T04:33:16.7736631Z     >>> # Example with subset of parameters
2025-03-14T04:33:16.7737012Z     >>> params_to_opt = [list(net.parameters())[0]]
2025-03-14T04:33:16.7737383Z     >>> net._register_fused_optim(
2025-03-14T04:33:16.7737830Z     ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2025-03-14T04:33:16.7738278Z     ... )
2025-03-14T04:33:16.7738511Z 
2025-03-14T04:33:16.7738769Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.7739155Z 
2025-03-14T04:33:16.7944092Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6.
2025-03-14T04:33:16.7945114Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.7945728Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2025-03-14T04:33:16.7946076Z 
2025-03-14T04:33:16.7946354Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-14T04:33:16.7947034Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-14T04:33:16.7947698Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-14T04:33:16.7948400Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-14T04:33:16.7948951Z 
2025-03-14T04:33:16.7949056Z     .. note::
2025-03-14T04:33:16.7949446Z         Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2025-03-14T04:33:16.7950295Z         than the utility function ``convert_conv2d_weight_memory_format``. Any
2025-03-14T04:33:16.7950892Z         layer with 4d weight will be affected by ``model.to``, which does not
2025-03-14T04:33:16.7951469Z         necessarily benefit from conversion to specified ``memory_format``.
2025-03-14T04:33:16.7952055Z         One place we are confident in is that NHWC(channels_last) conversion for
2025-03-14T04:33:16.7952637Z         convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2025-03-14T04:33:16.7953195Z         even in cases where we have to apply permutation to input tensors.
2025-03-14T04:33:16.7953530Z 
2025-03-14T04:33:16.7953761Z         Hence our strategy here is to convert only the weight of convolution to
2025-03-14T04:33:16.7954244Z         channels_last. This ensures that;
2025-03-14T04:33:16.7954704Z         1. Fast convolution kernels will be used, the benefit of which could
2025-03-14T04:33:16.7955290Z         outweigh overhead of permutation (if input is not in the same format).
2025-03-14T04:33:16.7955890Z         2. No unnecessary permutations are applied on layers that do not benefit
2025-03-14T04:33:16.7956372Z         from memory_format conversion.
2025-03-14T04:33:16.7956607Z 
2025-03-14T04:33:16.7956898Z         The optimal case is that, layers between convolution layers are channels
2025-03-14T04:33:16.7957489Z         last compatible. Input tensor would be permuted to channels last when it
2025-03-14T04:33:16.7958082Z         encounters the first convolution layer and stay in that memory format.
2025-03-14T04:33:16.7958682Z         Hence following convolutions will not need to permute its input tensor.
2025-03-14T04:33:16.7959040Z 
2025-03-14T04:33:16.7959338Z         In case where a channels last incompatible layer is between convolution
2025-03-14T04:33:16.7959910Z         layers, we need to permute the input tensor back to contiguous format
2025-03-14T04:33:16.7960482Z         for that layer. The input tensor will go through the remaining layers in
2025-03-14T04:33:16.7961066Z         contiguous format and be permuted to channels last when it encounters
2025-03-14T04:33:16.7961638Z         another convolution layer. There's no point in propagating that
2025-03-14T04:33:16.7962202Z         permutation to an earlier layer, as most layers are quite agnostic to
2025-03-14T04:33:16.7962661Z         ``memory_format``.
2025-03-14T04:33:16.7962840Z 
2025-03-14T04:33:16.7963092Z         This claim might change when PyTorch supports fusion of permutation, as
2025-03-14T04:33:16.7963679Z         there might have been a better spot to fuse the permutation other than
2025-03-14T04:33:16.7964156Z         immediately before a convolution.
2025-03-14T04:33:16.7964383Z 
2025-03-14T04:33:16.7964489Z     Args:
2025-03-14T04:33:16.7965059Z         module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2025-03-14T04:33:16.7965511Z                             ``nn.Module``
2025-03-14T04:33:16.7965880Z         memory_format: user specified ``memory_format``,
2025-03-14T04:33:16.7966338Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-14T04:33:16.7966649Z 
2025-03-14T04:33:16.7966744Z     Returns:
2025-03-14T04:33:16.7967035Z         The original module with updated ``nn.Conv2d``
2025-03-14T04:33:16.7967307Z 
2025-03-14T04:33:16.7967401Z     Example:
2025-03-14T04:33:16.7967695Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:16.7968116Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-14T04:33:16.7968623Z         >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda")
2025-03-14T04:33:16.7969095Z         >>> model = nn.Sequential(
2025-03-14T04:33:16.7969432Z         >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2025-03-14T04:33:16.7969792Z         >>> # This is identical to:
2025-03-14T04:33:16.7970258Z         >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-14T04:33:16.7970931Z         >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-14T04:33:16.7971436Z         >>> out = model(input)
2025-03-14T04:33:16.7971723Z     
2025-03-14T04:33:16.7972110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.7972481Z 
2025-03-14T04:33:16.7973153Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81.
2025-03-14T04:33:16.7974165Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.7974771Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2025-03-14T04:33:16.7975394Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-14T04:33:16.7976082Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-14T04:33:16.7976748Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-14T04:33:16.7977449Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-14T04:33:16.7977871Z 
2025-03-14T04:33:16.7977995Z     .. note::
2025-03-14T04:33:16.7978429Z         Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2025-03-14T04:33:16.7979033Z         than the utility function ``convert_conv3d_weight_memory_format``. Any
2025-03-14T04:33:16.7979609Z         layer with 4d weight will be affected by ``model.to``, which does not
2025-03-14T04:33:16.7980180Z         necessarily benefit from conversion to specified ``memory_format``.
2025-03-14T04:33:16.7980783Z         One place we are confident in is that NDHWC(channels_last_3d) conversion for
2025-03-14T04:33:16.7981395Z         convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2025-03-14T04:33:16.7981960Z         even in cases where we have to apply permutation to input tensors.
2025-03-14T04:33:16.7982286Z 
2025-03-14T04:33:16.7982534Z         Hence our strategy here is to convert only the weight of convolution to
2025-03-14T04:33:16.7983021Z         channels_last_3d. This ensures that;
2025-03-14T04:33:16.7983498Z         1. Fast convolution kernels will be used, the benefit of which could
2025-03-14T04:33:16.7984086Z         outweigh overhead of permutation (if input is not in the same format).
2025-03-14T04:33:16.7984691Z         2. No unnecessary permutations are applied on layers that do not benefit
2025-03-14T04:33:16.7985160Z         from memory_format conversion.
2025-03-14T04:33:16.7985394Z 
2025-03-14T04:33:16.7985624Z         The optimal case is that, layers between convolution layers are channels
2025-03-14T04:33:16.7986244Z         last compatible. Input tensor would be permuted to channels last when it
2025-03-14T04:33:16.7986875Z         encounters the first convolution layer and stay in that memory format.
2025-03-14T04:33:16.7987478Z         Hence following convolutions will not need to permute its input tensor.
2025-03-14T04:33:16.7987847Z 
2025-03-14T04:33:16.7988070Z         In case where a channels last incompatible layer is between convolution
2025-03-14T04:33:16.7988632Z         layers, we need to permute the input tensor back to contiguous format
2025-03-14T04:33:16.7989206Z         for that layer. The input tensor will go through the remaining layers in
2025-03-14T04:33:16.7989790Z         contiguous format and be permuted to channels last when it encounters
2025-03-14T04:33:16.7990356Z         another convolution layer. There's no point in propagating that
2025-03-14T04:33:16.7990914Z         permutation to an earlier layer, as most layers are quite agnostic to
2025-03-14T04:33:16.7991364Z         ``memory_format``.
2025-03-14T04:33:16.7991557Z 
2025-03-14T04:33:16.7991799Z         This claim might change when PyTorch supports fusion of permutation, as
2025-03-14T04:33:16.7992380Z         there might have been a better spot to fuse the permutation other than
2025-03-14T04:33:16.7992882Z         immediately before a convolution.
2025-03-14T04:33:16.7993124Z 
2025-03-14T04:33:16.7993216Z     Args:
2025-03-14T04:33:16.7993573Z         module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2025-03-14T04:33:16.7994028Z                             ``nn.Module``
2025-03-14T04:33:16.7994415Z         memory_format: user specified ``memory_format``,
2025-03-14T04:33:16.7994879Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-14T04:33:16.7995177Z 
2025-03-14T04:33:16.7995286Z     Returns:
2025-03-14T04:33:16.7995578Z         The original module with updated ``nn.Conv3d``
2025-03-14T04:33:16.7995830Z 
2025-03-14T04:33:16.7995937Z     Example:
2025-03-14T04:33:16.7996228Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:16.7996653Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-14T04:33:16.7997166Z         >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda")
2025-03-14T04:33:16.7997637Z         >>> model = nn.Sequential(
2025-03-14T04:33:16.7997973Z         >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2025-03-14T04:33:16.7998330Z         >>> # This is identical to:
2025-03-14T04:33:16.7998819Z         >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-14T04:33:16.7999571Z         >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-14T04:33:16.8000092Z         >>> out = model(input)
2025-03-14T04:33:16.8000384Z     
2025-03-14T04:33:16.8000776Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.8001160Z 
2025-03-14T04:33:16.8156922Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=935.
2025-03-14T04:33:16.8157856Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.8158481Z Prune tensor by removing random channels along the specified dimension.
2025-03-14T04:33:16.8158843Z 
2025-03-14T04:33:16.8159079Z     Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-14T04:33:16.8159705Z     by removing the specified ``amount`` of (currently unpruned) channels
2025-03-14T04:33:16.8160200Z     along the specified ``dim`` selected at random.
2025-03-14T04:33:16.8160670Z     Modifies module in place (and also return the modified module)
2025-03-14T04:33:16.8161084Z     by:
2025-03-14T04:33:16.8161224Z 
2025-03-14T04:33:16.8161458Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:16.8162018Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:16.8162578Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:16.8163301Z        original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:16.8163739Z        ``name+'_orig'``.
2025-03-14T04:33:16.8163927Z 
2025-03-14T04:33:16.8164018Z     Args:
2025-03-14T04:33:16.8164340Z         module (nn.Module): module containing the tensor to prune
2025-03-14T04:33:16.8164834Z         name (str): parameter name within ``module`` on which pruning
2025-03-14T04:33:16.8165245Z                 will act.
2025-03-14T04:33:16.8165605Z         amount (int or float): quantity of parameters to prune.
2025-03-14T04:33:16.8166084Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-14T04:33:16.8166607Z             fraction of parameters to prune. If ``int``, it represents the
2025-03-14T04:33:16.8167070Z             absolute number of parameters to prune.
2025-03-14T04:33:16.8167529Z         dim (int): index of the dim along which we define channels to prune.
2025-03-14T04:33:16.8167845Z 
2025-03-14T04:33:16.8167958Z     Returns:
2025-03-14T04:33:16.8168327Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-14T04:33:16.8168714Z 
2025-03-14T04:33:16.8168826Z     Examples:
2025-03-14T04:33:16.8169084Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:16.8169406Z         >>> m = prune.random_structured(
2025-03-14T04:33:16.8169790Z         ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
2025-03-14T04:33:16.8170149Z         ... )
2025-03-14T04:33:16.8170485Z         >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
2025-03-14T04:33:16.8170907Z         >>> print(columns_pruned)
2025-03-14T04:33:16.8171195Z         3
2025-03-14T04:33:16.8171422Z     
2025-03-14T04:33:16.8171811Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.8172192Z 
2025-03-14T04:33:16.8172716Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=976.
2025-03-14T04:33:16.8173606Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.8174308Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2025-03-14T04:33:16.8174749Z 
2025-03-14T04:33:16.8174989Z     Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-14T04:33:16.8175576Z     by removing the specified ``amount`` of (currently unpruned) channels
2025-03-14T04:33:16.8176157Z     along the specified ``dim`` with the lowest L\ ``n``-norm.
2025-03-14T04:33:16.8176666Z     Modifies module in place (and also return the modified module)
2025-03-14T04:33:16.8177088Z     by:
2025-03-14T04:33:16.8177230Z 
2025-03-14T04:33:16.8177448Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:16.8178014Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:16.8178579Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:16.8179144Z        original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:16.8179590Z        ``name+'_orig'``.
2025-03-14T04:33:16.8179764Z 
2025-03-14T04:33:16.8179873Z     Args:
2025-03-14T04:33:16.8180202Z         module (nn.Module): module containing the tensor to prune
2025-03-14T04:33:16.8180701Z         name (str): parameter name within ``module`` on which pruning
2025-03-14T04:33:16.8181118Z                 will act.
2025-03-14T04:33:16.8181483Z         amount (int or float): quantity of parameters to prune.
2025-03-14T04:33:16.8181968Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-14T04:33:16.8182483Z             fraction of parameters to prune. If ``int``, it represents the
2025-03-14T04:33:16.8182946Z             absolute number of parameters to prune.
2025-03-14T04:33:16.8183402Z         n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2025-03-14T04:33:16.8183948Z             entries for argument ``p`` in :func:`torch.norm`.
2025-03-14T04:33:16.8184467Z         dim (int): index of the dim along which we define channels to prune.
2025-03-14T04:33:16.8185024Z         importance_scores (torch.Tensor): tensor of importance scores (of same
2025-03-14T04:33:16.8185583Z             shape as module parameter) used to compute mask for pruning.
2025-03-14T04:33:16.8186126Z             The values in this tensor indicate the importance of the corresponding
2025-03-14T04:33:16.8186616Z             elements in the parameter being pruned.
2025-03-14T04:33:16.8187102Z             If unspecified or None, the module parameter will be used in its place.
2025-03-14T04:33:16.8187463Z 
2025-03-14T04:33:16.8187556Z     Returns:
2025-03-14T04:33:16.8187929Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-14T04:33:16.8188282Z 
2025-03-14T04:33:16.8188378Z     Examples:
2025-03-14T04:33:16.8188655Z         >>> from torch.nn.utils import prune
2025-03-14T04:33:16.8189011Z         >>> m = prune.ln_structured(
2025-03-14T04:33:16.8189425Z         ...     nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf')
2025-03-14T04:33:16.8189831Z         ... )
2025-03-14T04:33:16.8190096Z     
2025-03-14T04:33:16.8190480Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.8190845Z 
2025-03-14T04:33:16.8191429Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1023.
2025-03-14T04:33:16.8192354Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.8192742Z 
2025-03-14T04:33:16.8193174Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2025-03-14T04:33:16.8193732Z 
2025-03-14T04:33:16.8193854Z Modifies modules in place by:
2025-03-14T04:33:16.8194069Z 
2025-03-14T04:33:16.8194285Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:16.8194854Z    binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:16.8195419Z 2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:16.8195969Z    original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:16.8196401Z    ``name+'_orig'``.
2025-03-14T04:33:16.8196578Z 
2025-03-14T04:33:16.8196670Z Args:
2025-03-14T04:33:16.8197009Z     parameters (Iterable of (module, name) tuples): parameters of
2025-03-14T04:33:16.8197563Z         the model to prune in a global fashion, i.e. by aggregating all
2025-03-14T04:33:16.8198098Z         weights prior to deciding which ones to prune. module must be of
2025-03-14T04:33:16.8198590Z         type :class:`nn.Module`, and name must be a string.
2025-03-14T04:33:16.8199092Z     pruning_method (function): a valid pruning function from this module,
2025-03-14T04:33:16.8199717Z         or a custom one implemented by the user that satisfies the
2025-03-14T04:33:16.8200258Z         implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2025-03-14T04:33:16.8200855Z     importance_scores (dict): a dictionary mapping (module, name) tuples to
2025-03-14T04:33:16.8201449Z         the corresponding parameter's importance scores tensor. The tensor
2025-03-14T04:33:16.8202017Z         should be the same shape as the parameter, and is used for computing
2025-03-14T04:33:16.8202461Z         mask for pruning.
2025-03-14T04:33:16.8202868Z         If unspecified or None, the parameter will be used in place of its
2025-03-14T04:33:16.8203312Z         importance scores.
2025-03-14T04:33:16.8203620Z     kwargs: other keyword arguments such as:
2025-03-14T04:33:16.8204065Z         amount (int or float): quantity of parameters to prune across the
2025-03-14T04:33:16.8204503Z         specified parameters.
2025-03-14T04:33:16.8204885Z         If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-14T04:33:16.8205393Z         fraction of parameters to prune. If ``int``, it represents the
2025-03-14T04:33:16.8205917Z         absolute number of parameters to prune.
2025-03-14T04:33:16.8206174Z 
2025-03-14T04:33:16.8206266Z Raises:
2025-03-14T04:33:16.8206562Z     TypeError: if ``PRUNING_TYPE != 'unstructured'``
2025-03-14T04:33:16.8206838Z 
2025-03-14T04:33:16.8206927Z Note:
2025-03-14T04:33:16.8207279Z     Since global structured pruning doesn't make much sense unless the
2025-03-14T04:33:16.8207826Z     norm is normalized by the size of the parameter, we now limit the
2025-03-14T04:33:16.8208310Z     scope of global pruning to unstructured methods.
2025-03-14T04:33:16.8208574Z 
2025-03-14T04:33:16.8208684Z Examples:
2025-03-14T04:33:16.8208947Z     >>> from torch.nn.utils import prune
2025-03-14T04:33:16.8209310Z     >>> from collections import OrderedDict
2025-03-14T04:33:16.8209674Z     >>> net = nn.Sequential(OrderedDict([
2025-03-14T04:33:16.8210029Z     ...     ('first', nn.Linear(10, 4)),
2025-03-14T04:33:16.8210369Z     ...     ('second', nn.Linear(4, 1)),
2025-03-14T04:33:16.8210687Z     ... ]))
2025-03-14T04:33:16.8210935Z     >>> parameters_to_prune = (
2025-03-14T04:33:16.8211235Z     ...     (net.first, 'weight'),
2025-03-14T04:33:16.8211592Z     ...     (net.second, 'weight'),
2025-03-14T04:33:16.8211896Z     ... )
2025-03-14T04:33:16.8212148Z     >>> prune.global_unstructured(
2025-03-14T04:33:16.8212476Z     ...     parameters_to_prune,
2025-03-14T04:33:16.8212822Z     ...     pruning_method=prune.L1Unstructured,
2025-03-14T04:33:16.8213178Z     ...     amount=10,
2025-03-14T04:33:16.8213448Z     ... )
2025-03-14T04:33:16.8213813Z     >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2025-03-14T04:33:16.8226041Z     tensor(10)
2025-03-14T04:33:16.8226321Z 
2025-03-14T04:33:16.8226326Z 
2025-03-14T04:33:16.8226633Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.8227013Z 
2025-03-14T04:33:16.8227572Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1142.
2025-03-14T04:33:16.8228483Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.8229270Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2025-03-14T04:33:16.8229774Z 
2025-03-14T04:33:16.8230009Z     Modifies module in place (and also return the modified module) by:
2025-03-14T04:33:16.8230343Z 
2025-03-14T04:33:16.8230669Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:16.8231243Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:16.8231811Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:16.8232350Z        original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:16.8232795Z        ``name+'_orig'``.
2025-03-14T04:33:16.8232983Z 
2025-03-14T04:33:16.8233079Z     Args:
2025-03-14T04:33:16.8233412Z         module (nn.Module): module containing the tensor to prune
2025-03-14T04:33:16.8233912Z         name (str): parameter name within ``module`` on which pruning
2025-03-14T04:33:16.8234327Z             will act.
2025-03-14T04:33:16.8234687Z         mask (Tensor): binary mask to be applied to the parameter.
2025-03-14T04:33:16.8234999Z 
2025-03-14T04:33:16.8235096Z     Returns:
2025-03-14T04:33:16.8235472Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-14T04:33:16.8235830Z 
2025-03-14T04:33:16.8235926Z     Examples:
2025-03-14T04:33:16.8236202Z         >>> from torch.nn.utils import prune
2025-03-14T04:33:16.8236565Z         >>> m = prune.custom_from_mask(
2025-03-14T04:33:16.8236970Z         ...     nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0])
2025-03-14T04:33:16.8237362Z         ... )
2025-03-14T04:33:16.8237612Z         >>> print(m.bias_mask)
2025-03-14T04:33:16.8237917Z         tensor([0., 1., 0.])
2025-03-14T04:33:16.8238098Z 
2025-03-14T04:33:16.8238242Z     
2025-03-14T04:33:16.8238661Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.8239033Z 
2025-03-14T04:33:16.9205668Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=117.
2025-03-14T04:33:16.9206579Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.9207340Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2025-03-14T04:33:16.9207821Z 
2025-03-14T04:33:16.9208087Z     Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2025-03-14T04:33:16.9208689Z     Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2025-03-14T04:33:16.9209263Z     Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2025-03-14T04:33:16.9209707Z     (UAI 2018).
2025-03-14T04:33:16.9209852Z 
2025-03-14T04:33:16.9210098Z     Exponential Moving Average is a variation of `Polyak averaging`_,
2025-03-14T04:33:16.9210696Z     but using exponential weights instead of equal weights across iterations.
2025-03-14T04:33:16.9211204Z 
2025-03-14T04:33:16.9211465Z     AveragedModel class creates a copy of the provided module :attr:`model`
2025-03-14T04:33:16.9212066Z     on the device :attr:`device` and allows to compute running averages of the
2025-03-14T04:33:16.9212549Z     parameters of the :attr:`model`.
2025-03-14T04:33:16.9212763Z 
2025-03-14T04:33:16.9212879Z     Args:
2025-03-14T04:33:16.9213182Z         model (torch.nn.Module): model to use with SWA/EMA
2025-03-14T04:33:16.9213688Z         device (torch.device, optional): if provided, the averaged model will be
2025-03-14T04:33:16.9214175Z             stored on the :attr:`device`
2025-03-14T04:33:16.9214622Z         avg_fn (function, optional): the averaging function used to update
2025-03-14T04:33:16.9215171Z             parameters; the function must take in the current value of the
2025-03-14T04:33:16.9215737Z             :class:`AveragedModel` parameter, the current value of :attr:`model`
2025-03-14T04:33:16.9216297Z             parameter, and the number of models already averaged; if None,
2025-03-14T04:33:16.9216801Z             an equally weighted average is used (default: None)
2025-03-14T04:33:16.9217325Z         multi_avg_fn (function, optional): the averaging function used to update
2025-03-14T04:33:16.9217924Z             parameters inplace; the function must take in the current values of the
2025-03-14T04:33:16.9218619Z             :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2025-03-14T04:33:16.9219255Z             parameters as a list, and the number of models already averaged; if None,
2025-03-14T04:33:16.9219787Z             an equally weighted average is used (default: None)
2025-03-14T04:33:16.9220287Z         use_buffers (bool): if ``True``, it will compute running averages for
2025-03-14T04:33:16.9220855Z             both the parameters and the buffers of the model. (default: ``False``)
2025-03-14T04:33:16.9221215Z 
2025-03-14T04:33:16.9221313Z     Example:
2025-03-14T04:33:16.9221602Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:16.9221997Z         >>> loader, optimizer, model, loss_fn = ...
2025-03-14T04:33:16.9222434Z         >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2025-03-14T04:33:16.9222963Z         >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2025-03-14T04:33:16.9223476Z         >>>                                     T_max=300)
2025-03-14T04:33:16.9223827Z         >>> swa_start = 160
2025-03-14T04:33:16.9224167Z         >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2025-03-14T04:33:16.9224543Z         >>> for i in range(300):
2025-03-14T04:33:16.9224872Z         >>>      for input, target in loader:
2025-03-14T04:33:16.9225230Z         >>>          optimizer.zero_grad()
2025-03-14T04:33:16.9225609Z         >>>          loss_fn(model(input), target).backward()
2025-03-14T04:33:16.9226079Z         >>>          optimizer.step()
2025-03-14T04:33:16.9226414Z         >>>      if i > swa_start:
2025-03-14T04:33:16.9226761Z         >>>          swa_model.update_parameters(model)
2025-03-14T04:33:16.9227140Z         >>>          swa_scheduler.step()
2025-03-14T04:33:16.9227472Z         >>>      else:
2025-03-14T04:33:16.9227738Z         >>>          scheduler.step()
2025-03-14T04:33:16.9228055Z         >>>
2025-03-14T04:33:16.9228362Z         >>> # Update bn statistics for the swa_model at the end
2025-03-14T04:33:16.9228819Z         >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2025-03-14T04:33:16.9229114Z 
2025-03-14T04:33:16.9229420Z     You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2025-03-14T04:33:16.9230055Z     If no averaging function is provided, the default is to compute
2025-03-14T04:33:16.9230536Z     equally-weighted average of the weights (SWA).
2025-03-14T04:33:16.9230816Z 
2025-03-14T04:33:16.9230912Z     Example:
2025-03-14T04:33:16.9231201Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:16.9231669Z         >>> # Compute exponential moving averages of the weights and buffers
2025-03-14T04:33:16.9232220Z         >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2025-03-14T04:33:16.9232739Z         >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2025-03-14T04:33:16.9233095Z 
2025-03-14T04:33:16.9233218Z     .. note::
2025-03-14T04:33:16.9233596Z         When using SWA/EMA with models containing Batch Normalization you may
2025-03-14T04:33:16.9234161Z         need to update the activation statistics for Batch Normalization.
2025-03-14T04:33:16.9234738Z         This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2025-03-14T04:33:16.9235336Z         or by setting :attr:`use_buffers` to `True`. The first approach updates the
2025-03-14T04:33:16.9235945Z         statistics in a post-training step by passing data through the model. The
2025-03-14T04:33:16.9236562Z         second does it during the parameter update phase by averaging all buffers.
2025-03-14T04:33:16.9237184Z         Empirical evidence has shown that updating the statistics in normalization
2025-03-14T04:33:16.9237801Z         layers increases accuracy, but you may wish to empirically test which
2025-03-14T04:33:16.9238327Z         approach yields the best results in your problem.
2025-03-14T04:33:16.9238598Z 
2025-03-14T04:33:16.9238710Z     .. note::
2025-03-14T04:33:16.9239229Z         :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2025-03-14T04:33:16.9239610Z 
2025-03-14T04:33:16.9239725Z     .. note::
2025-03-14T04:33:16.9240081Z         When :meth:`update_parameters` is called for the first time (i.e.
2025-03-14T04:33:16.9240607Z         :attr:`n_averaged` is `0`) the parameters of `model` are copied
2025-03-14T04:33:16.9241139Z         to the parameters of :class:`AveragedModel`. For every subsequent
2025-03-14T04:33:16.9241674Z         call of :meth:`update_parameters` the function `avg_fn` is used
2025-03-14T04:33:16.9242093Z         to update the parameters.
2025-03-14T04:33:16.9242316Z 
2025-03-14T04:33:16.9242552Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-14T04:33:16.9243047Z         https://arxiv.org/abs/1803.05407
2025-03-14T04:33:16.9243531Z     .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2025-03-14T04:33:16.9243991Z         Average:
2025-03-14T04:33:16.9244274Z         https://arxiv.org/abs/1806.05594
2025-03-14T04:33:16.9244718Z     .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2025-03-14T04:33:16.9245164Z         https://arxiv.org/abs/1904.11943
2025-03-14T04:33:16.9245630Z     .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2025-03-14T04:33:16.9246091Z         Generalizes Well:
2025-03-14T04:33:16.9246402Z         https://arxiv.org/abs/2001.02312
2025-03-14T04:33:16.9246745Z     .. _Polyak averaging:
2025-03-14T04:33:16.9247174Z         https://paperswithcode.com/method/polyak-averaging
2025-03-14T04:33:16.9247560Z     
2025-03-14T04:33:16.9247950Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.9248322Z 
2025-03-14T04:33:16.9248982Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=369.
2025-03-14T04:33:16.9249848Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:16.9250460Z Anneals the learning rate in each parameter group to a fixed value.
2025-03-14T04:33:16.9250792Z 
2025-03-14T04:33:16.9251045Z     This learning rate scheduler is meant to be used with Stochastic Weight
2025-03-14T04:33:16.9251628Z     Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2025-03-14T04:33:16.9251959Z 
2025-03-14T04:33:16.9252072Z     Args:
2025-03-14T04:33:16.9252394Z         optimizer (torch.optim.Optimizer): wrapped optimizer
2025-03-14T04:33:16.9252916Z         swa_lrs (float or list): the learning rate value for all param groups
2025-03-14T04:33:16.9253387Z             together or separately for each group.
2025-03-14T04:33:16.9253913Z         annealing_epochs (int): number of epochs in the annealing phase
2025-03-14T04:33:16.9254335Z             (default: 10)
2025-03-14T04:33:16.9254747Z         annealing_strategy (str): "cos" or "linear"; specifies the annealing
2025-03-14T04:33:16.9255318Z             strategy: "cos" for cosine annealing, "linear" for linear annealing
2025-03-14T04:33:16.9255768Z             (default: "cos")
2025-03-14T04:33:16.9256154Z         last_epoch (int): the index of the last epoch (default: -1)
2025-03-14T04:33:16.9256460Z 
2025-03-14T04:33:16.9256647Z     The :class:`SWALR` scheduler can be used together with other
2025-03-14T04:33:16.9257183Z     schedulers to switch to a constant learning rate late in the training
2025-03-14T04:33:16.9257641Z     as in the example below.
2025-03-14T04:33:16.9257844Z 
2025-03-14T04:33:16.9257942Z     Example:
2025-03-14T04:33:16.9258225Z         >>> # xdoctest: +SKIP("Undefined variables")
2025-03-14T04:33:16.9258605Z         >>> loader, optimizer, model = ...
2025-03-14T04:33:16.9258963Z         >>> lr_lambda = lambda epoch: 0.9
2025-03-14T04:33:16.9259426Z         >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2025-03-14T04:33:16.9259890Z         >>>        lr_lambda=lr_lambda)
2025-03-14T04:33:16.9260336Z         >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2025-03-14T04:33:16.9260824Z         >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2025-03-14T04:33:16.9261240Z         >>> swa_start = 160
2025-03-14T04:33:16.9261545Z         >>> for i in range(300):
2025-03-14T04:33:16.9261874Z         >>>      for input, target in loader:
2025-03-14T04:33:16.9262237Z         >>>          optimizer.zero_grad()
2025-03-14T04:33:16.9262613Z         >>>          loss_fn(model(input), target).backward()
2025-03-14T04:33:16.9262997Z         >>>          optimizer.step()
2025-03-14T04:33:16.9263308Z         >>>      if i > swa_start:
2025-03-14T04:33:16.9263633Z         >>>          swa_scheduler.step()
2025-03-14T04:33:16.9263960Z         >>>      else:
2025-03-14T04:33:16.9264240Z         >>>          scheduler.step()
2025-03-14T04:33:16.9264464Z 
2025-03-14T04:33:16.9264692Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-14T04:33:16.9264830Z         https://arxiv.org/abs/1803.05407
2025-03-14T04:33:16.9264930Z     
2025-03-14T04:33:16.9265204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:16.9265209Z 
2025-03-14T04:33:17.3926074Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1263.
2025-03-14T04:33:17.3927641Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:17.3928195Z Asserts that ``actual`` and ``expected`` are close.
2025-03-14T04:33:17.3928733Z 
2025-03-14T04:33:17.3929115Z     If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2025-03-14T04:33:17.3929621Z 
2025-03-14T04:33:17.3929739Z     .. math::
2025-03-14T04:33:17.3929894Z 
2025-03-14T04:33:17.3930267Z         \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2025-03-14T04:33:17.3930761Z 
2025-03-14T04:33:17.3931116Z     Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2025-03-14T04:33:17.3931800Z     only considered equal to each other if ``equal_nan`` is ``True``.
2025-03-14T04:33:17.3932138Z 
2025-03-14T04:33:17.3932343Z     In addition, they are only considered close if they have the same
2025-03-14T04:33:17.3932673Z 
2025-03-14T04:33:17.3932877Z     - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2025-03-14T04:33:17.3933337Z     - ``dtype`` (if ``check_dtype`` is ``True``),
2025-03-14T04:33:17.3933745Z     - ``layout`` (if ``check_layout`` is ``True``), and
2025-03-14T04:33:17.3934142Z     - stride (if ``check_stride`` is ``True``).
2025-03-14T04:33:17.3934454Z 
2025-03-14T04:33:17.3934755Z     If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2025-03-14T04:33:17.3935189Z 
2025-03-14T04:33:17.3935555Z     If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2025-03-14T04:33:17.3936415Z     checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2025-03-14T04:33:17.3937149Z     or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2025-03-14T04:33:17.3937894Z     are always checked for equality whereas the values are checked for closeness according to the definition above.
2025-03-14T04:33:17.3938408Z 
2025-03-14T04:33:17.3938697Z     If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2025-03-14T04:33:17.3939461Z     :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2025-03-14T04:33:17.3940046Z     definition above.
2025-03-14T04:33:17.3940224Z 
2025-03-14T04:33:17.3940534Z     ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2025-03-14T04:33:17.3941398Z     :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2025-03-14T04:33:17.3942262Z     have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2025-03-14T04:33:17.3943123Z     or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2025-03-14T04:33:17.3943853Z     their elements are considered close according to the above definition.
2025-03-14T04:33:17.3944213Z 
2025-03-14T04:33:17.3944317Z     .. note::
2025-03-14T04:33:17.3944474Z 
2025-03-14T04:33:17.3944811Z         Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2025-03-14T04:33:17.3945597Z         :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2025-03-14T04:33:17.3946329Z         Python scalars of different types can be checked, but require ``check_dtype=False``.
2025-03-14T04:33:17.3946737Z 
2025-03-14T04:33:17.3946831Z     Args:
2025-03-14T04:33:17.3947080Z         actual (Any): Actual input.
2025-03-14T04:33:17.3947420Z         expected (Any): Expected input.
2025-03-14T04:33:17.3948013Z         allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2025-03-14T04:33:17.3948660Z             are allowed. Otherwise type equality is required.
2025-03-14T04:33:17.3949557Z         rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2025-03-14T04:33:17.3950426Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-14T04:33:17.3951183Z         atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2025-03-14T04:33:17.3951934Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-14T04:33:17.3952580Z         equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2025-03-14T04:33:17.3953245Z         check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2025-03-14T04:33:17.3953915Z             :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2025-03-14T04:33:17.3954530Z             :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2025-03-14T04:33:17.3955246Z         check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2025-03-14T04:33:17.3956067Z             check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2025-03-14T04:33:17.3956744Z             :func:`torch.promote_types`) before being compared.
2025-03-14T04:33:17.3957385Z         check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2025-03-14T04:33:17.3958201Z             check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2025-03-14T04:33:17.3958758Z             compared.
2025-03-14T04:33:17.3959356Z         check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2025-03-14T04:33:17.3960203Z         msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2025-03-14T04:33:17.3961042Z             the comparison. Can also passed as callable in which case it will be called with the generated message and
2025-03-14T04:33:17.3961652Z             should return the new message.
2025-03-14T04:33:17.3961880Z 
2025-03-14T04:33:17.3961987Z     Raises:
2025-03-14T04:33:17.3962366Z         ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2025-03-14T04:33:17.3962898Z         ValueError: If only ``rtol`` or ``atol`` is specified.
2025-03-14T04:33:17.3963515Z         AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2025-03-14T04:33:17.3964372Z         AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2025-03-14T04:33:17.3964973Z             different types.
2025-03-14T04:33:17.3965537Z         AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2025-03-14T04:33:17.3966384Z         AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2025-03-14T04:33:17.3967191Z         AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2025-03-14T04:33:17.3967942Z         AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2025-03-14T04:33:17.3968497Z             :attr:`~torch.Tensor.layout`.
2025-03-14T04:33:17.3968944Z         AssertionError: If only one of corresponding tensors is quantized.
2025-03-14T04:33:17.3969681Z         AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2025-03-14T04:33:17.3970486Z         AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2025-03-14T04:33:17.3971031Z             :attr:`~torch.Tensor.device`.
2025-03-14T04:33:17.3971599Z         AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2025-03-14T04:33:17.3972416Z         AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2025-03-14T04:33:17.3973329Z         AssertionError: If the values of corresponding tensors are not close according to the definition above.
2025-03-14T04:33:17.3973825Z 
2025-03-14T04:33:17.3974192Z     The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2025-03-14T04:33:17.3974833Z     ``dtype``'s, the maximum of both tolerances is used.
2025-03-14T04:33:17.3975111Z 
2025-03-14T04:33:17.3975251Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3975642Z     | ``dtype``                 | ``rtol``   | ``atol`` |
2025-03-14T04:33:17.3976010Z     +===========================+============+==========+
2025-03-14T04:33:17.3976397Z     | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2025-03-14T04:33:17.3976798Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3977198Z     | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2025-03-14T04:33:17.3977599Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3977996Z     | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:17.3978395Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3978817Z     | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2025-03-14T04:33:17.3979207Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3979596Z     | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2025-03-14T04:33:17.3979993Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3980386Z     | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:17.3980778Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3981169Z     | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2025-03-14T04:33:17.3981561Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3981953Z     | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:17.3982346Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3982737Z     | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:17.3983133Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3983511Z     | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:17.3983903Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3984298Z     | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:17.3984721Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3985112Z     | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:17.3985501Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3985880Z     | other                     | ``0.0``    | ``0.0``  |
2025-03-14T04:33:17.3986259Z     +---------------------------+------------+----------+
2025-03-14T04:33:17.3986514Z 
2025-03-14T04:33:17.3986615Z     .. note::
2025-03-14T04:33:17.3986768Z 
2025-03-14T04:33:17.3987158Z         :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2025-03-14T04:33:17.3988027Z         to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2025-03-14T04:33:17.3988768Z         define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2025-03-14T04:33:17.3989158Z 
2025-03-14T04:33:17.3989266Z         >>> import functools
2025-03-14T04:33:17.3989726Z         >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2025-03-14T04:33:17.3990227Z         >>> assert_equal(1e-9, 1e-10)
2025-03-14T04:33:17.3990574Z         Traceback (most recent call last):
2025-03-14T04:33:17.3990910Z         ...
2025-03-14T04:33:17.3991178Z         AssertionError: Scalars are not equal!
2025-03-14T04:33:17.3991524Z         <BLANKLINE>
2025-03-14T04:33:17.3991798Z         Expected 1e-10 but got 1e-09.
2025-03-14T04:33:17.3992219Z         Absolute difference: 9.000000000000001e-10
2025-03-14T04:33:17.3992582Z         Relative difference: 9.0
2025-03-14T04:33:17.3992779Z 
2025-03-14T04:33:17.3992892Z     Examples:
2025-03-14T04:33:17.3993158Z         >>> # tensor to tensor comparison
2025-03-14T04:33:17.3993517Z         >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2025-03-14T04:33:17.3993921Z         >>> actual = torch.acos(torch.cos(expected))
2025-03-14T04:33:17.3994336Z         >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:17.3994609Z 
2025-03-14T04:33:17.3994729Z         >>> # scalar to scalar comparison
2025-03-14T04:33:17.3995060Z         >>> import math
2025-03-14T04:33:17.3995348Z         >>> expected = math.sqrt(2.0)
2025-03-14T04:33:17.3995682Z         >>> actual = 2.0 / math.sqrt(2.0)
2025-03-14T04:33:17.3996061Z         >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:17.3996329Z 
2025-03-14T04:33:17.3996461Z         >>> # numpy array to numpy array comparison
2025-03-14T04:33:17.3996824Z         >>> import numpy as np
2025-03-14T04:33:17.3997150Z         >>> expected = np.array([1e0, 1e-1, 1e-2])
2025-03-14T04:33:17.3997525Z         >>> actual = np.arccos(np.cos(expected))
2025-03-14T04:33:17.3997957Z         >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:17.3998214Z 
2025-03-14T04:33:17.3998356Z         >>> # sequence to sequence comparison
2025-03-14T04:33:17.3998704Z         >>> import numpy as np
2025-03-14T04:33:17.3999249Z         >>> # The types of the sequences do not have to match. They only have to have the same
2025-03-14T04:33:17.3999778Z         >>> # length and their elements have to match.
2025-03-14T04:33:17.4000197Z         >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2025-03-14T04:33:17.4000592Z         >>> actual = tuple(expected)
2025-03-14T04:33:17.4000964Z         >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:17.4001226Z 
2025-03-14T04:33:17.4001369Z         >>> # mapping to mapping comparison
2025-03-14T04:33:17.4001742Z         >>> from collections import OrderedDict
2025-03-14T04:33:17.4002083Z         >>> import numpy as np
2025-03-14T04:33:17.4002395Z         >>> foo = torch.tensor(1.0)
2025-03-14T04:33:17.4002713Z         >>> bar = 2.0
2025-03-14T04:33:17.4002988Z         >>> baz = np.array(3.0)
2025-03-14T04:33:17.4003443Z         >>> # The types and a possible ordering of mappings do not have to match. They only
2025-03-14T04:33:17.4004067Z         >>> # have to have the same set of keys and their elements have to match.
2025-03-14T04:33:17.4004610Z         >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2025-03-14T04:33:17.4005087Z         >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2025-03-14T04:33:17.4005503Z         >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:17.4005783Z 
2025-03-14T04:33:17.4005918Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-14T04:33:17.4006294Z         >>> actual = expected.clone()
2025-03-14T04:33:17.4006694Z         >>> # By default, directly related instances can be compared
2025-03-14T04:33:17.4007213Z         >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2025-03-14T04:33:17.4007761Z         >>> # This check can be made more strict with allow_subclasses=False
2025-03-14T04:33:17.4008202Z         >>> torch.testing.assert_close(
2025-03-14T04:33:17.4008640Z         ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2025-03-14T04:33:17.4009061Z         ... )
2025-03-14T04:33:17.4009332Z         Traceback (most recent call last):
2025-03-14T04:33:17.4009663Z         ...
2025-03-14T04:33:17.4010012Z         TypeError: No comparison pair was able to handle inputs of type
2025-03-14T04:33:17.4010565Z         <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2025-03-14T04:33:17.4011138Z         >>> # If the inputs are not directly related, they are never considered close
2025-03-14T04:33:17.4011671Z         >>> torch.testing.assert_close(actual.numpy(), expected)
2025-03-14T04:33:17.4012186Z         Traceback (most recent call last):
2025-03-14T04:33:17.4012525Z         ...
2025-03-14T04:33:17.4012961Z         TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2025-03-14T04:33:17.4013496Z         and <class 'torch.Tensor'>.
2025-03-14T04:33:17.4013987Z         >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2025-03-14T04:33:17.4014515Z         >>> # their type if check_dtype=False.
2025-03-14T04:33:17.4014943Z         >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2025-03-14T04:33:17.4015232Z 
2025-03-14T04:33:17.4015360Z         >>> # NaN != NaN by default.
2025-03-14T04:33:17.4015717Z         >>> expected = torch.tensor(float("Nan"))
2025-03-14T04:33:17.4016090Z         >>> actual = expected.clone()
2025-03-14T04:33:17.4016469Z         >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:17.4016866Z         Traceback (most recent call last):
2025-03-14T04:33:17.4017201Z         ...
2025-03-14T04:33:17.4017483Z         AssertionError: Scalars are not close!
2025-03-14T04:33:17.4017818Z         <BLANKLINE>
2025-03-14T04:33:17.4018129Z         Expected nan but got nan.
2025-03-14T04:33:17.4018497Z         Absolute difference: nan (up to 1e-05 allowed)
2025-03-14T04:33:17.4018924Z         Relative difference: nan (up to 1.3e-06 allowed)
2025-03-14T04:33:17.4019407Z         >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2025-03-14T04:33:17.4019744Z 
2025-03-14T04:33:17.4019879Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-14T04:33:17.4020251Z         >>> actual = torch.tensor([1.0, 4.0, 5.0])
2025-03-14T04:33:17.4020646Z         >>> # The default error message can be overwritten.
2025-03-14T04:33:17.4021204Z         >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
2025-03-14T04:33:17.4021742Z         Traceback (most recent call last):
2025-03-14T04:33:17.4022068Z         ...
2025-03-14T04:33:17.4022366Z         AssertionError: Argh, the tensors are not close!
2025-03-14T04:33:17.4022869Z         >>> # If msg is a callable, it can be used to augment the generated message with
2025-03-14T04:33:17.4023329Z         >>> # extra information
2025-03-14T04:33:17.4023647Z         >>> torch.testing.assert_close(
2025-03-14T04:33:17.4024086Z         ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2025-03-14T04:33:17.4024506Z         ... )
2025-03-14T04:33:17.4024803Z         Traceback (most recent call last):
2025-03-14T04:33:17.4025131Z         ...
2025-03-14T04:33:17.4025383Z         AssertionError: Header
2025-03-14T04:33:17.4025672Z         <BLANKLINE>
2025-03-14T04:33:17.4025949Z         Tensor-likes are not close!
2025-03-14T04:33:17.4026269Z         <BLANKLINE>
2025-03-14T04:33:17.4026546Z         Mismatched elements: 2 / 3 (66.7%)
2025-03-14T04:33:17.4027009Z         Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2025-03-14T04:33:17.4027605Z         Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2025-03-14T04:33:17.4028061Z         <BLANKLINE>
2025-03-14T04:33:17.4028311Z         Footer
2025-03-14T04:33:17.4028549Z     
2025-03-14T04:33:17.4028934Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:17.4029303Z 
2025-03-14T04:33:18.5045921Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=104.
2025-03-14T04:33:18.5046917Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5047462Z Register a container-like type as pytree node.
2025-03-14T04:33:18.5047723Z 
2025-03-14T04:33:18.5047833Z     Args:
2025-03-14T04:33:18.5048169Z         cls (type): A Python type to treat as an internal pytree node.
2025-03-14T04:33:18.5048890Z         flatten_fn (callable): A function to be used during flattening, taking an instance of
2025-03-14T04:33:18.5049715Z             ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2025-03-14T04:33:18.5050461Z             recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2025-03-14T04:33:18.5051019Z             passed to the ``unflatten_fn``.
2025-03-14T04:33:18.5051544Z         unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2025-03-14T04:33:18.5052229Z             returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2025-03-14T04:33:18.5052785Z             The function should return an instance of ``cls``.
2025-03-14T04:33:18.5053332Z         serialized_type_name (str, optional): A keyword argument used to specify the fully
2025-03-14T04:33:18.5053903Z             qualified name used when serializing the tree spec.
2025-03-14T04:33:18.5054503Z         to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2025-03-14T04:33:18.5055232Z             to convert the context of the pytree to a custom json dumpable representation. This is
2025-03-14T04:33:18.5055915Z             used for json serialization, which is being used in :mod:`torch.export` right now.
2025-03-14T04:33:18.5056669Z         from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2025-03-14T04:33:18.5057370Z             how to convert the custom json dumpable representation of the context back to the
2025-03-14T04:33:18.5058016Z             original context. This is used for json deserialization, which is being used in
2025-03-14T04:33:18.5058528Z             :mod:`torch.export` right now.
2025-03-14T04:33:18.5058773Z 
2025-03-14T04:33:18.5058894Z     Example::
2025-03-14T04:33:18.5059046Z 
2025-03-14T04:33:18.5059154Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:18.5059504Z         >>> # Registry a Python type with lambda functions
2025-03-14T04:33:18.5059885Z         >>> register_pytree_node(
2025-03-14T04:33:18.5060193Z         ...     set,
2025-03-14T04:33:18.5060490Z         ...     lambda s: (sorted(s), None, None),
2025-03-14T04:33:18.5060875Z         ...     lambda children, _: set(children),
2025-03-14T04:33:18.5061216Z         ... )
2025-03-14T04:33:18.5061458Z     
2025-03-14T04:33:18.5061846Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5062215Z 
2025-03-14T04:33:18.5576963Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1200.
2025-03-14T04:33:18.5577980Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5578379Z 
2025-03-14T04:33:18.5578601Z Context passed to policy function during selective checkpointing.
2025-03-14T04:33:18.5578945Z 
2025-03-14T04:33:18.5579176Z This class is used to pass relevant metadata to the policy function during
2025-03-14T04:33:18.5579799Z selective checkpointing. The metadata includes whether the current invocation
2025-03-14T04:33:18.5580370Z of the policy function is during recomputation or not.
2025-03-14T04:33:18.5580659Z 
2025-03-14T04:33:18.5580753Z Example:
2025-03-14T04:33:18.5580999Z     >>> # xdoctest: +SKIP(stub)
2025-03-14T04:33:18.5581303Z     >>>
2025-03-14T04:33:18.5581564Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-14T04:33:18.5582010Z     >>>    print(ctx.is_recompute)
2025-03-14T04:33:18.5582486Z     >>>
2025-03-14T04:33:18.5583212Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-14T04:33:18.5583869Z     >>>
2025-03-14T04:33:18.5584150Z     >>> out = torch.utils.checkpoint.checkpoint(
2025-03-14T04:33:18.5584520Z     >>>     fn, x, y,
2025-03-14T04:33:18.5584787Z     >>>     use_reentrant=False,
2025-03-14T04:33:18.5585101Z     >>>     context_fn=context_fn,
2025-03-14T04:33:18.5585406Z     >>> )
2025-03-14T04:33:18.5585547Z 
2025-03-14T04:33:18.5585805Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5586187Z 
2025-03-14T04:33:18.5587002Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1334.
2025-03-14T04:33:18.5588021Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5588413Z 
2025-03-14T04:33:18.5588669Z Helper to avoid recomputing certain ops during activation checkpointing.
2025-03-14T04:33:18.5589023Z 
2025-03-14T04:33:18.5589262Z Use this with `torch.utils.checkpoint.checkpoint` to control which
2025-03-14T04:33:18.5589758Z operations are recomputed during the backward pass.
2025-03-14T04:33:18.5590049Z 
2025-03-14T04:33:18.5590138Z Args:
2025-03-14T04:33:18.5590395Z     policy_fn_or_list (Callable or List):
2025-03-14T04:33:18.5590809Z       - If a policy function is provided, it should accept a
2025-03-14T04:33:18.5591353Z         :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2025-03-14T04:33:18.5591936Z         kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2025-03-14T04:33:18.5592518Z         indicating whether the execution of the op should be recomputed or not.
2025-03-14T04:33:18.5593147Z       - If a list of operations is provided, it is equivalent to a policy
2025-03-14T04:33:18.5593670Z         returning `CheckpointPolicy.MUST_SAVE` for the specified
2025-03-14T04:33:18.5594206Z         operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2025-03-14T04:33:18.5594650Z         operations.
2025-03-14T04:33:18.5595047Z     allow_cache_entry_mutation (bool, optional): By default, an error is
2025-03-14T04:33:18.5595611Z         raised if any tensors cached by selective activation checkpoint are
2025-03-14T04:33:18.5596167Z         mutated in order to ensure correctness. If set to `True`, this check
2025-03-14T04:33:18.5596604Z         is disabled.
2025-03-14T04:33:18.5596864Z Returns:
2025-03-14T04:33:18.5597117Z     A tuple of two context managers.
2025-03-14T04:33:18.5597331Z 
2025-03-14T04:33:18.5597444Z Example:
2025-03-14T04:33:18.5597691Z     >>> # xdoctest: +REQUIRES(LINUX)
2025-03-14T04:33:18.5598016Z     >>> import functools
2025-03-14T04:33:18.5598295Z     >>>
2025-03-14T04:33:18.5598543Z     >>> x = torch.rand(10, 10, requires_grad=True)
2025-03-14T04:33:18.5598924Z     >>> y = torch.rand(10, 10, requires_grad=True)
2025-03-14T04:33:18.5599357Z     >>>
2025-03-14T04:33:18.5599592Z     >>> ops_to_save = [
2025-03-14T04:33:18.5599892Z     >>>    torch.ops.aten.mm.default,
2025-03-14T04:33:18.5600253Z     >>> ]
2025-03-14T04:33:18.5600478Z     >>>
2025-03-14T04:33:18.5600740Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-14T04:33:18.5601097Z     >>>    if op in ops_to_save:
2025-03-14T04:33:18.5601435Z     >>>        return CheckpointPolicy.MUST_SAVE
2025-03-14T04:33:18.5601778Z     >>>    else:
2025-03-14T04:33:18.5602084Z     >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2025-03-14T04:33:18.5602447Z     >>>
2025-03-14T04:33:18.5602858Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-14T04:33:18.5603357Z     >>>
2025-03-14T04:33:18.5603579Z     >>> # or equivalently
2025-03-14T04:33:18.5604042Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2025-03-14T04:33:18.5604541Z     >>>
2025-03-14T04:33:18.5604773Z     >>> def fn(x, y):
2025-03-14T04:33:18.5605155Z     >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2025-03-14T04:33:18.5605573Z     >>>
2025-03-14T04:33:18.5605851Z     >>> out = torch.utils.checkpoint.checkpoint(
2025-03-14T04:33:18.5606213Z     >>>     fn, x, y,
2025-03-14T04:33:18.5606491Z     >>>     use_reentrant=False,
2025-03-14T04:33:18.5606808Z     >>>     context_fn=context_fn,
2025-03-14T04:33:18.5607112Z     >>> )
2025-03-14T04:33:18.5607237Z 
2025-03-14T04:33:18.5607507Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5607877Z 
2025-03-14T04:33:18.5805475Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1064.
2025-03-14T04:33:18.5806502Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5806900Z 
2025-03-14T04:33:18.5807050Z Create a :class:`setuptools.Extension` for C++.
2025-03-14T04:33:18.5807321Z 
2025-03-14T04:33:18.5807565Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-14T04:33:18.5808163Z bare minimum (but often sufficient) arguments to build a C++ extension.
2025-03-14T04:33:18.5808514Z 
2025-03-14T04:33:18.5808726Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-14T04:33:18.5809205Z constructor. Full list arguments can be found at
2025-03-14T04:33:18.5809840Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-14T04:33:18.5810282Z 
2025-03-14T04:33:18.5810419Z .. warning::
2025-03-14T04:33:18.5810847Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-14T04:33:18.5811565Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-14T04:33:18.5812114Z     the user's responsibility in their library to not use APIs from
2025-03-14T04:33:18.5812721Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-14T04:33:18.5813307Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-14T04:33:18.5813884Z     example, to give access to custom ops from python, the library should
2025-03-14T04:33:18.5814370Z     register the ops through the dispatcher.
2025-03-14T04:33:18.5814626Z 
2025-03-14T04:33:18.5814858Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-14T04:33:18.5815424Z     as a compile flag when py_limited_api is specified as an option for
2025-03-14T04:33:18.5816025Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-14T04:33:18.5816659Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-14T04:33:18.5817228Z     safety, and sanity in order to encourage best practices. To target a
2025-03-14T04:33:18.5817791Z     different version, set min_supported_cpython to the hexcode of the
2025-03-14T04:33:18.5818254Z     CPython version of choice.
2025-03-14T04:33:18.5818464Z 
2025-03-14T04:33:18.5818560Z Example:
2025-03-14T04:33:18.5818803Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:18.5819146Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:18.5819592Z     >>> from setuptools import setup
2025-03-14T04:33:18.5820042Z     >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2025-03-14T04:33:18.5820482Z     >>> setup(
2025-03-14T04:33:18.5820738Z     ...     name='extension',
2025-03-14T04:33:18.5821036Z     ...     ext_modules=[
2025-03-14T04:33:18.5821321Z     ...         CppExtension(
2025-03-14T04:33:18.5821631Z     ...             name='extension',
2025-03-14T04:33:18.5821956Z     ...             sources=['extension.cpp'],
2025-03-14T04:33:18.5822327Z     ...             extra_compile_args=['-g'],
2025-03-14T04:33:18.5822725Z     ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2025-03-14T04:33:18.5823096Z     ...     ],
2025-03-14T04:33:18.5823341Z     ...     cmdclass={
2025-03-14T04:33:18.5823631Z     ...         'build_ext': BuildExtension
2025-03-14T04:33:18.5823957Z     ...     })
2025-03-14T04:33:18.5824110Z 
2025-03-14T04:33:18.5824367Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5824755Z 
2025-03-14T04:33:18.5825504Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1134.
2025-03-14T04:33:18.5826590Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5826970Z 
2025-03-14T04:33:18.5827152Z Create a :class:`setuptools.Extension` for CUDA/C++.
2025-03-14T04:33:18.5827424Z 
2025-03-14T04:33:18.5827813Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-14T04:33:18.5828421Z bare minimum (but often sufficient) arguments to build a CUDA/C++
2025-03-14T04:33:18.5828989Z extension. This includes the CUDA include path, library path and runtime
2025-03-14T04:33:18.5829447Z library.
2025-03-14T04:33:18.5829573Z 
2025-03-14T04:33:18.5829801Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-14T04:33:18.5830284Z constructor. Full list arguments can be found at
2025-03-14T04:33:18.5830868Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-14T04:33:18.5831323Z 
2025-03-14T04:33:18.5831431Z .. warning::
2025-03-14T04:33:18.5831811Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-14T04:33:18.5832380Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-14T04:33:18.5832917Z     the user's responsibility in their library to not use APIs from
2025-03-14T04:33:18.5833480Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-14T04:33:18.5834062Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-14T04:33:18.5834700Z     example, to give access to custom ops from python, the library should
2025-03-14T04:33:18.5835343Z     register the ops through the dispatcher.
2025-03-14T04:33:18.5835627Z 
2025-03-14T04:33:18.5835857Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-14T04:33:18.5836420Z     as a compile flag when py_limited_api is specified as an option for
2025-03-14T04:33:18.5836964Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-14T04:33:18.5837519Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-14T04:33:18.5838075Z     safety, and sanity in order to encourage best practices. To target a
2025-03-14T04:33:18.5838638Z     different version, set min_supported_cpython to the hexcode of the
2025-03-14T04:33:18.5839093Z     CPython version of choice.
2025-03-14T04:33:18.5839371Z 
2025-03-14T04:33:18.5839482Z Example:
2025-03-14T04:33:18.5839728Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:18.5840073Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:18.5840464Z     >>> from setuptools import setup
2025-03-14T04:33:18.5840918Z     >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2025-03-14T04:33:18.5841359Z     >>> setup(
2025-03-14T04:33:18.5841615Z     ...     name='cuda_extension',
2025-03-14T04:33:18.5841982Z     ...     ext_modules=[
2025-03-14T04:33:18.5842256Z     ...         CUDAExtension(
2025-03-14T04:33:18.5842580Z     ...                 name='cuda_extension',
2025-03-14T04:33:18.5842989Z     ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-14T04:33:18.5843420Z     ...                 extra_compile_args={'cxx': ['-g'],
2025-03-14T04:33:18.5843803Z     ...                                     'nvcc': ['-O2']},
2025-03-14T04:33:18.5844217Z     ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2025-03-14T04:33:18.5844599Z     ...     ],
2025-03-14T04:33:18.5844848Z     ...     cmdclass={
2025-03-14T04:33:18.5845142Z     ...         'build_ext': BuildExtension
2025-03-14T04:33:18.5845474Z     ...     })
2025-03-14T04:33:18.5845608Z 
2025-03-14T04:33:18.5845728Z Compute capabilities:
2025-03-14T04:33:18.5845897Z 
2025-03-14T04:33:18.5846215Z By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-14T04:33:18.5846935Z building process of the extension, plus PTX. If down the road a new card is installed the
2025-03-14T04:33:18.5847646Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2025-03-14T04:33:18.5848367Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
2025-03-14T04:33:18.5849379Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2025-03-14T04:33:18.5850004Z support (see below for details on PTX).
2025-03-14T04:33:18.5850279Z 
2025-03-14T04:33:18.5850611Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2025-03-14T04:33:18.5851180Z CCs you want the extension to support:
2025-03-14T04:33:18.5851404Z 
2025-03-14T04:33:18.5851617Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2025-03-14T04:33:18.5852176Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2025-03-14T04:33:18.5852538Z 
2025-03-14T04:33:18.5852876Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2025-03-14T04:33:18.5853644Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2025-03-14T04:33:18.5854397Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2025-03-14T04:33:18.5855125Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2025-03-14T04:33:18.5855876Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2025-03-14T04:33:18.5856604Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2025-03-14T04:33:18.5857375Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2025-03-14T04:33:18.5858146Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2025-03-14T04:33:18.5858701Z "8.0 8.6" would be better.
2025-03-14T04:33:18.5858877Z 
2025-03-14T04:33:18.5859196Z Note that while it's possible to include all supported archs, the more archs get included the
2025-03-14T04:33:18.5859959Z slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-14T04:33:18.5860369Z 
2025-03-14T04:33:18.5860717Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2025-03-14T04:33:18.5861395Z To workaround the issue, move python binding logic to pure C++ file.
2025-03-14T04:33:18.5861722Z 
2025-03-14T04:33:18.5861835Z Example use:
2025-03-14T04:33:18.5862084Z     #include <ATen/ATen.h>
2025-03-14T04:33:18.5862445Z     at::Tensor SigmoidAlphaBlendForwardCuda(....)
2025-03-14T04:33:18.5862719Z 
2025-03-14T04:33:18.5862813Z Instead of:
2025-03-14T04:33:18.5863070Z     #include <torch/extension.h>
2025-03-14T04:33:18.5863446Z     torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2025-03-14T04:33:18.5863730Z 
2025-03-14T04:33:18.5864082Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2025-03-14T04:33:18.5864989Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2025-03-14T04:33:18.5865619Z 
2025-03-14T04:33:18.5865736Z Relocatable device code linking:
2025-03-14T04:33:18.5865957Z 
2025-03-14T04:33:18.5866239Z If you want to reference device symbols across compilation units (across object files),
2025-03-14T04:33:18.5866910Z the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2025-03-14T04:33:18.5867657Z An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2025-03-14T04:33:18.5868476Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2025-03-14T04:33:18.5869251Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2025-03-14T04:33:18.5869887Z helps reduce the protentional perf degradation of `-rdc`.
2025-03-14T04:33:18.5870363Z Note that it needs to be used at both steps to be useful.
2025-03-14T04:33:18.5870657Z 
2025-03-14T04:33:18.5871028Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2025-03-14T04:33:18.5871701Z There is also a case where `-dlink` is used without `-rdc`:
2025-03-14T04:33:18.5872303Z when an extension is linked against a static lib containing rdc-compiled objects
2025-03-14T04:33:18.5872940Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2025-03-14T04:33:18.5873279Z 
2025-03-14T04:33:18.5873489Z Note: Ninja is required to build a CUDA Extension with RDC linking.
2025-03-14T04:33:18.5873819Z 
2025-03-14T04:33:18.5873914Z Example:
2025-03-14T04:33:18.5874158Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:18.5874501Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:18.5874883Z     >>> CUDAExtension(
2025-03-14T04:33:18.5875169Z     ...        name='cuda_extension',
2025-03-14T04:33:18.5875561Z     ...        sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-14T04:33:18.5875950Z     ...        dlink=True,
2025-03-14T04:33:18.5876262Z     ...        dlink_libraries=["dlink_lib"],
2025-03-14T04:33:18.5876635Z     ...        extra_compile_args={'cxx': ['-g'],
2025-03-14T04:33:18.5877016Z     ...                            'nvcc': ['-O2', '-rdc=true']})
2025-03-14T04:33:18.5877259Z 
2025-03-14T04:33:18.5877536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5877908Z 
2025-03-14T04:33:18.5878467Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1325.
2025-03-14T04:33:18.5879515Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5879892Z 
2025-03-14T04:33:18.5880078Z Creates a :class:`setuptools.Extension` for SYCL/C++.
2025-03-14T04:33:18.5880355Z 
2025-03-14T04:33:18.5880617Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-14T04:33:18.5881195Z bare minimum (but often sufficient) arguments to build a SYCL/C++
2025-03-14T04:33:18.5881619Z extension.
2025-03-14T04:33:18.5881750Z 
2025-03-14T04:33:18.5881961Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-14T04:33:18.5882385Z constructor.
2025-03-14T04:33:18.5882545Z 
2025-03-14T04:33:18.5882642Z .. warning::
2025-03-14T04:33:18.5883024Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-14T04:33:18.5883593Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-14T04:33:18.5884136Z     the user's responsibility in their library to not use APIs from
2025-03-14T04:33:18.5884693Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-14T04:33:18.5885316Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-14T04:33:18.5885873Z     example, to give access to custom ops from python, the library should
2025-03-14T04:33:18.5886346Z     register the ops through the dispatcher.
2025-03-14T04:33:18.5886604Z 
2025-03-14T04:33:18.5886834Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-14T04:33:18.5887391Z     as a compile flag when py_limited_api is specified as an option for
2025-03-14T04:33:18.5887935Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-14T04:33:18.5888491Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-14T04:33:18.5889049Z     safety, and sanity in order to encourage best practices. To target a
2025-03-14T04:33:18.5889611Z     different version, set min_supported_cpython to the hexcode of the
2025-03-14T04:33:18.5890068Z     CPython version of choice.
2025-03-14T04:33:18.5890262Z 
2025-03-14T04:33:18.5890370Z Example:
2025-03-14T04:33:18.5890612Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:18.5890956Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:18.5891461Z     >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension
2025-03-14T04:33:18.5891909Z     >>> setup(
2025-03-14T04:33:18.5892179Z     ...     name='xpu_extension',
2025-03-14T04:33:18.5892495Z     ...     ext_modules=[
2025-03-14T04:33:18.5892768Z     ...     SyclExtension(
2025-03-14T04:33:18.5893077Z     ...                 name='xpu_extension',
2025-03-14T04:33:18.5893527Z     ...                 sources=['extension.cpp', 'extension_kernel.cpp'],
2025-03-14T04:33:18.5894041Z     ...                 extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
2025-03-14T04:33:18.5894446Z     ...     ],
2025-03-14T04:33:18.5894694Z     ...     cmdclass={
2025-03-14T04:33:18.5895000Z     ...         'build_ext': BuildExtension
2025-03-14T04:33:18.5895330Z     ...     })
2025-03-14T04:33:18.5895484Z 
2025-03-14T04:33:18.5895786Z By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-14T04:33:18.5896469Z building process of the extension. If down the road a new card is installed the
2025-03-14T04:33:18.5897110Z extension may need to be recompiled. You can override the default behavior using
2025-03-14T04:33:18.5897803Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension
2025-03-14T04:33:18.5898340Z to support:
2025-03-14T04:33:18.5898474Z 
2025-03-14T04:33:18.5898715Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py``
2025-03-14T04:33:18.5899035Z 
2025-03-14T04:33:18.5899357Z Note that while it's possible to include all supported archs, the more archs get included the
2025-03-14T04:33:18.5900115Z slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-14T04:33:18.5900525Z 
2025-03-14T04:33:18.5900691Z Note: Ninja is required to build SyclExtension.
2025-03-14T04:33:18.5900945Z 
2025-03-14T04:33:18.5901221Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5901589Z 
2025-03-14T04:33:18.5902167Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1502.
2025-03-14T04:33:18.5903050Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5903445Z 
2025-03-14T04:33:18.5903596Z Load a PyTorch C++ extension just-in-time (JIT).
2025-03-14T04:33:18.5903873Z 
2025-03-14T04:33:18.5904089Z To load an extension, a Ninja build file is emitted, which is used to
2025-03-14T04:33:18.5904641Z compile the given sources into a dynamic library. This library is
2025-03-14T04:33:18.5905207Z subsequently loaded into the current Python process as a module and
2025-03-14T04:33:18.5905693Z returned from this function, ready for use.
2025-03-14T04:33:18.5905948Z 
2025-03-14T04:33:18.5906161Z By default, the directory to which the build file is emitted and the
2025-03-14T04:33:18.5906764Z resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2025-03-14T04:33:18.5907333Z ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2025-03-14T04:33:18.5907884Z the name of the extension. This location can be overridden in two ways.
2025-03-14T04:33:18.5908433Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2025-03-14T04:33:18.5908985Z replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2025-03-14T04:33:18.5909554Z into subfolders of this directory. Second, if the ``build_directory``
2025-03-14T04:33:18.5910132Z argument to this function is supplied, it overrides the entire path, i.e.
2025-03-14T04:33:18.5910661Z the library will be compiled into that folder directly.
2025-03-14T04:33:18.5910949Z 
2025-03-14T04:33:18.5911162Z To compile the sources, the default system compiler (``c++``) is used,
2025-03-14T04:33:18.5911739Z which can be overridden by setting the ``CXX`` environment variable. To pass
2025-03-14T04:33:18.5912336Z additional arguments to the compilation process, ``extra_cflags`` or
2025-03-14T04:33:18.5912918Z ``extra_ldflags`` can be provided. For example, to compile your extension
2025-03-14T04:33:18.5913496Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2025-03-14T04:33:18.5913998Z ``extra_cflags`` to pass further include directories.
2025-03-14T04:33:18.5914267Z 
2025-03-14T04:33:18.5914529Z CUDA support with mixed compilation is provided. Simply pass CUDA source
2025-03-14T04:33:18.5915114Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2025-03-14T04:33:18.5915721Z detected and compiled with nvcc rather than the C++ compiler. This includes
2025-03-14T04:33:18.5916307Z passing the CUDA lib64 directory as a library directory, and linking
2025-03-14T04:33:18.5916801Z ``cudart``. You can pass additional flags to nvcc via
2025-03-14T04:33:18.5917283Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2025-03-14T04:33:18.5917858Z heuristics for finding the CUDA install directory are used, which usually
2025-03-14T04:33:18.5918439Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2025-03-14T04:33:18.5918882Z safest option.
2025-03-14T04:33:18.5919026Z 
2025-03-14T04:33:18.5919364Z SYCL support with mixed compilation is provided. Simply pass SYCL source
2025-03-14T04:33:18.5919941Z files (``.sycl``) along with other sources. Such files will be detected
2025-03-14T04:33:18.5920480Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather
2025-03-14T04:33:18.5921044Z than the C++ compiler. You can pass additional flags to SYCL compiler
2025-03-14T04:33:18.5921581Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++.
2025-03-14T04:33:18.5922147Z SYCL compiler is expected to be found via system PATH environment
2025-03-14T04:33:18.5922570Z variable.
2025-03-14T04:33:18.5922712Z 
2025-03-14T04:33:18.5922802Z Args:
2025-03-14T04:33:18.5923151Z     name: The name of the extension to build. This MUST be the same as the
2025-03-14T04:33:18.5923608Z         name of the pybind11 module!
2025-03-14T04:33:18.5924035Z     sources: A list of relative or absolute paths to C++ source files.
2025-03-14T04:33:18.5924588Z     extra_cflags: optional list of compiler flags to forward to the build.
2025-03-14T04:33:18.5925162Z     extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2025-03-14T04:33:18.5925623Z         when building CUDA sources.
2025-03-14T04:33:18.5926064Z     extra_sycl_cflags: optional list of compiler flags to forward to SYCL
2025-03-14T04:33:18.5926540Z         compiler when building SYCL sources.
2025-03-14T04:33:18.5927003Z     extra_ldflags: optional list of linker flags to forward to the build.
2025-03-14T04:33:18.5927574Z     extra_include_paths: optional list of include directories to forward
2025-03-14T04:33:18.5928019Z         to the build.
2025-03-14T04:33:18.5928372Z     build_directory: optional path to use as build workspace.
2025-03-14T04:33:18.5928888Z     verbose: If ``True``, turns on verbose logging of load steps.
2025-03-14T04:33:18.5929418Z     with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-14T04:33:18.5929932Z         the build. If set to ``None`` (default), this value is
2025-03-14T04:33:18.5930425Z         automatically determined based on the existence of ``.cu`` or
2025-03-14T04:33:18.5930928Z         ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2025-03-14T04:33:18.5931327Z         and libraries to be included.
2025-03-14T04:33:18.5931781Z     with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-14T04:33:18.5932302Z         the build. If set to ``None`` (default), this value is
2025-03-14T04:33:18.5932805Z         automatically determined based on the existence of ``.sycl`` in
2025-03-14T04:33:18.5933318Z         ``sources``. Set it to `True`` to force SYCL headers and
2025-03-14T04:33:18.5933718Z         libraries to be included.
2025-03-14T04:33:18.5934142Z     is_python_module: If ``True`` (default), imports the produced shared
2025-03-14T04:33:18.5934671Z         library as a Python module. If ``False``, behavior depends on
2025-03-14T04:33:18.5935096Z         ``is_standalone``.
2025-03-14T04:33:18.5935499Z     is_standalone: If ``False`` (default) loads the constructed extension
2025-03-14T04:33:18.5936041Z         into the process as a plain dynamic library. If ``True``, build a
2025-03-14T04:33:18.5936478Z         standalone executable.
2025-03-14T04:33:18.5936685Z 
2025-03-14T04:33:18.5936778Z Returns:
2025-03-14T04:33:18.5937090Z     If ``is_python_module`` is ``True``:
2025-03-14T04:33:18.5937506Z         Returns the loaded PyTorch extension as a Python module.
2025-03-14T04:33:18.5937801Z 
2025-03-14T04:33:18.5938025Z     If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2025-03-14T04:33:18.5938575Z         Returns nothing. (The shared library is loaded into the process as
2025-03-14T04:33:18.5939019Z         a side effect.)
2025-03-14T04:33:18.5939189Z 
2025-03-14T04:33:18.5939326Z     If ``is_standalone`` is ``True``.
2025-03-14T04:33:18.5939760Z         Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2025-03-14T04:33:18.5940273Z         added to the PATH environment variable as a side effect.)
2025-03-14T04:33:18.5940564Z 
2025-03-14T04:33:18.5940676Z Example:
2025-03-14T04:33:18.5940923Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:18.5941259Z     >>> from torch.utils.cpp_extension import load
2025-03-14T04:33:18.5941610Z     >>> module = load(
2025-03-14T04:33:18.5951689Z     ...     name='extension',
2025-03-14T04:33:18.5952202Z     ...     sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-14T04:33:18.5952610Z     ...     extra_cflags=['-O2'],
2025-03-14T04:33:18.5953065Z     ...     verbose=True)
2025-03-14T04:33:18.5953240Z 
2025-03-14T04:33:18.5953521Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5953892Z 
2025-03-14T04:33:18.5954447Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1811.
2025-03-14T04:33:18.5955358Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.5955734Z 
2025-03-14T04:33:18.5955968Z Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2025-03-14T04:33:18.5956292Z 
2025-03-14T04:33:18.5956544Z This function behaves exactly like :func:`load`, but takes its sources as
2025-03-14T04:33:18.5957136Z strings rather than filenames. These strings are stored to files in the
2025-03-14T04:33:18.5957718Z build directory, after which the behavior of :func:`load_inline` is
2025-03-14T04:33:18.5958168Z identical to :func:`load`.
2025-03-14T04:33:18.5958354Z 
2025-03-14T04:33:18.5958449Z See `the
2025-03-14T04:33:18.5958927Z tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2025-03-14T04:33:18.5959594Z for good examples of using this function.
2025-03-14T04:33:18.5959846Z 
2025-03-14T04:33:18.5960144Z Sources may omit two required parts of a typical non-inline C++ extension:
2025-03-14T04:33:18.5960756Z the necessary header includes, as well as the (pybind11) binding code. More
2025-03-14T04:33:18.5961373Z precisely, strings passed to ``cpp_sources`` are first concatenated into a
2025-03-14T04:33:18.5961940Z single ``.cpp`` file. This file is then prepended with ``#include
2025-03-14T04:33:18.5962364Z <torch/extension.h>``.
2025-03-14T04:33:18.5962550Z 
2025-03-14T04:33:18.5962784Z Furthermore, if the ``functions`` argument is supplied, bindings will be
2025-03-14T04:33:18.5963386Z automatically generated for each function specified. ``functions`` can
2025-03-14T04:33:18.5963975Z either be a list of function names, or a dictionary mapping from function
2025-03-14T04:33:18.5964555Z names to docstrings. If a list is given, the name of each function is used
2025-03-14T04:33:18.5965010Z as its docstring.
2025-03-14T04:33:18.5965179Z 
2025-03-14T04:33:18.5965398Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2025-03-14T04:33:18.5965932Z file and  prepended with ``torch/types.h``, ``cuda.h`` and
2025-03-14T04:33:18.5966446Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2025-03-14T04:33:18.5967011Z separately, but ultimately linked into a single library. Note that no
2025-03-14T04:33:18.5967597Z bindings are generated for functions in ``cuda_sources`` per se. To bind
2025-03-14T04:33:18.5968180Z to a CUDA kernel, you must create a C++ function that calls it, and either
2025-03-14T04:33:18.5968834Z declare or define this C++ function in one of the ``cpp_sources`` (and
2025-03-14T04:33:18.5969298Z include its name in ``functions``).
2025-03-14T04:33:18.5969512Z 
2025-03-14T04:33:18.5969752Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl``
2025-03-14T04:33:18.5970325Z file and  prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes.
2025-03-14T04:33:18.5970881Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately
2025-03-14T04:33:18.5971433Z linked into a single library. Note that no bindings are generated for
2025-03-14T04:33:18.5971999Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must
2025-03-14T04:33:18.5972561Z create a C++ function that calls it, and either declare or define this
2025-03-14T04:33:18.5973096Z C++ function in one of the ``cpp_sources`` (and include its name
2025-03-14T04:33:18.5973513Z in ``functions``).
2025-03-14T04:33:18.5973669Z 
2025-03-14T04:33:18.5973862Z See :func:`load` for a description of arguments omitted below.
2025-03-14T04:33:18.5974176Z 
2025-03-14T04:33:18.5974271Z Args:
2025-03-14T04:33:18.5974630Z     cpp_sources: A string, or list of strings, containing C++ source code.
2025-03-14T04:33:18.5975238Z     cuda_sources: A string, or list of strings, containing CUDA source code.
2025-03-14T04:33:18.5975828Z     sycl_sources: A string, or list of strings, containing SYCL source code.
2025-03-14T04:33:18.5976389Z     functions: A list of function names for which to generate function
2025-03-14T04:33:18.5976943Z         bindings. If a dictionary is given, it should map function names to
2025-03-14T04:33:18.5977474Z         docstrings (which are otherwise just the function names).
2025-03-14T04:33:18.5978008Z     with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-14T04:33:18.5978517Z         the build. If set to ``None`` (default), this value is
2025-03-14T04:33:18.5979015Z         automatically determined based on whether ``cuda_sources`` is
2025-03-14T04:33:18.5979510Z         provided. Set it to ``True`` to force CUDA headers
2025-03-14T04:33:18.5979906Z         and libraries to be included.
2025-03-14T04:33:18.5980358Z     with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-14T04:33:18.5980879Z         the build. If set to ``None`` (default), this value is
2025-03-14T04:33:18.5981374Z         automatically determined based on whether ``sycl_sources`` is
2025-03-14T04:33:18.5981899Z         provided. Set it to ``True`` to force SYCL headers
2025-03-14T04:33:18.5982300Z         and libraries to be included.
2025-03-14T04:33:18.5982742Z     with_pytorch_error_handling: Determines whether pytorch error and
2025-03-14T04:33:18.5983288Z         warning macros are handled by pytorch instead of pybind. To do
2025-03-14T04:33:18.5983836Z         this, each function ``foo`` is called via an intermediary ``_safe_foo``
2025-03-14T04:33:18.5984394Z         function. This redirection might cause issues in obscure cases
2025-03-14T04:33:18.5984922Z         of cpp. This flag should be set to ``False`` when this redirect
2025-03-14T04:33:18.5985339Z         causes issues.
2025-03-14T04:33:18.5985503Z 
2025-03-14T04:33:18.5985618Z Example:
2025-03-14T04:33:18.5985898Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:18.5986337Z     >>> from torch.utils.cpp_extension import load_inline
2025-03-14T04:33:18.5986714Z     >>> source = """
2025-03-14T04:33:18.5987048Z     at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2025-03-14T04:33:18.5987425Z       return x.sin() + y.sin();
2025-03-14T04:33:18.5987721Z     }
2025-03-14T04:33:18.5987945Z     """
2025-03-14T04:33:18.5988230Z     >>> module = load_inline(name='inline_extension',
2025-03-14T04:33:18.5988628Z     ...                      cpp_sources=[source],
2025-03-14T04:33:18.5989005Z     ...                      functions=['sin_add'])
2025-03-14T04:33:18.5989239Z 
2025-03-14T04:33:18.5989349Z .. note::
2025-03-14T04:33:18.5989760Z     Since load_inline will just-in-time compile the source code, please ensure
2025-03-14T04:33:18.5990393Z     that you have the right toolchains installed in the runtime. For example,
2025-03-14T04:33:18.5990989Z     when loading C++, make sure a C++ compiler is available. If you're loading
2025-03-14T04:33:18.5991592Z     a CUDA extension, you will need to additionally install the corresponding CUDA
2025-03-14T04:33:18.5992229Z     toolkit (nvcc and any other dependencies your code has). Compiling toolchains
2025-03-14T04:33:18.5992859Z     are not included when you install torch and must be additionally installed.
2025-03-14T04:33:18.5993216Z 
2025-03-14T04:33:18.5993492Z     During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
2025-03-14T04:33:18.5994104Z     the extension. This may use up too many resources on some systems. One
2025-03-14T04:33:18.5994684Z     can control the number of workers by setting the `MAX_JOBS` environment
2025-03-14T04:33:18.5995162Z     variable to a non-negative number.
2025-03-14T04:33:18.5995384Z 
2025-03-14T04:33:18.5995660Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.5996030Z 
2025-03-14T04:33:18.7639073Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61.
2025-03-14T04:33:18.7640278Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.7640682Z 
2025-03-14T04:33:18.7641013Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2025-03-14T04:33:18.7641440Z 
2025-03-14T04:33:18.7641742Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2025-03-14T04:33:18.7642427Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2025-03-14T04:33:18.7643050Z server like load. It can emulate multiple calling threads to a single module
2025-03-14T04:33:18.7643664Z provided. In the future we plan to enhance this component to support inter and
2025-03-14T04:33:18.7644298Z intra-op parallelism as well as multiple models running in a single process.
2025-03-14T04:33:18.7644681Z 
2025-03-14T04:33:18.7644944Z Please note that even though nn.Module is supported, it might incur an overhead
2025-03-14T04:33:18.7645549Z from the need to hold GIL every time we execute Python code or pass around
2025-03-14T04:33:18.7646148Z inputs as Python objects. As soon as you have a ScriptModule version of your
2025-03-14T04:33:18.7646817Z model for inference deployment it is better to switch to using it in this
2025-03-14T04:33:18.7647282Z benchmark.
2025-03-14T04:33:18.7647420Z 
2025-03-14T04:33:18.7647534Z Example::
2025-03-14T04:33:18.7647665Z 
2025-03-14T04:33:18.7647809Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:18.7648205Z     >>> from torch.utils import ThroughputBenchmark
2025-03-14T04:33:18.7648616Z     >>> bench = ThroughputBenchmark(my_module)
2025-03-14T04:33:18.7649198Z     >>> # Pre-populate benchmark's data set with the inputs
2025-03-14T04:33:18.7649609Z     >>> for input in inputs:
2025-03-14T04:33:18.7650042Z     ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2025-03-14T04:33:18.7650520Z     ...     bench.add_input(input[0], x2=input[1])
2025-03-14T04:33:18.7650988Z     >>> # Inputs supplied above are randomly used during the execution
2025-03-14T04:33:18.7651435Z     >>> stats = bench.benchmark(
2025-03-14T04:33:18.7651769Z     ...     num_calling_threads=4,
2025-03-14T04:33:18.7652107Z     ...     num_warmup_iters = 100,
2025-03-14T04:33:18.7652439Z     ...     num_iters = 1000,
2025-03-14T04:33:18.7652737Z     ... )
2025-03-14T04:33:18.7653064Z     >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2025-03-14T04:33:18.7653558Z     >>> print("Number of iterations: {}".format(stats.num_iters))
2025-03-14T04:33:18.7653874Z 
2025-03-14T04:33:18.7654132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.7654516Z 
2025-03-14T04:33:18.8561963Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18.
2025-03-14T04:33:18.8562995Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:18.8563600Z Sampler that restricts data loading to a subset of the dataset.
2025-03-14T04:33:18.8563935Z 
2025-03-14T04:33:18.8564084Z     It is especially useful in conjunction with
2025-03-14T04:33:18.8564613Z     :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2025-03-14T04:33:18.8565258Z     process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2025-03-14T04:33:18.8565887Z     :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2025-03-14T04:33:18.8566386Z     original dataset that is exclusive to it.
2025-03-14T04:33:18.8566647Z 
2025-03-14T04:33:18.8566761Z     .. note::
2025-03-14T04:33:18.8567157Z         Dataset is assumed to be of constant size and that any instance of it always
2025-03-14T04:33:18.8567682Z         returns the same elements in the same order.
2025-03-14T04:33:18.8567951Z 
2025-03-14T04:33:18.8568134Z     Args:
2025-03-14T04:33:18.8568408Z         dataset: Dataset used for sampling.
2025-03-14T04:33:18.8568879Z         num_replicas (int, optional): Number of processes participating in
2025-03-14T04:33:18.8569486Z             distributed training. By default, :attr:`world_size` is retrieved from the
2025-03-14T04:33:18.8569996Z             current distributed group.
2025-03-14T04:33:18.8570478Z         rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2025-03-14T04:33:18.8571072Z             By default, :attr:`rank` is retrieved from the current distributed
2025-03-14T04:33:18.8571504Z             group.
2025-03-14T04:33:18.8571897Z         shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2025-03-14T04:33:18.8572354Z             indices.
2025-03-14T04:33:18.8572724Z         seed (int, optional): random seed used to shuffle the sampler if
2025-03-14T04:33:18.8573333Z             :attr:`shuffle=True`. This number should be identical across all
2025-03-14T04:33:18.8573848Z             processes in the distributed group. Default: ``0``.
2025-03-14T04:33:18.8574366Z         drop_last (bool, optional): if ``True``, then the sampler will drop the
2025-03-14T04:33:18.8574919Z             tail of the data to make it evenly divisible across the number of
2025-03-14T04:33:18.8575509Z             replicas. If ``False``, the sampler will add extra indices to make
2025-03-14T04:33:18.8576055Z             the data evenly divisible across the replicas. Default: ``False``.
2025-03-14T04:33:18.8576405Z 
2025-03-14T04:33:18.8576505Z     .. warning::
2025-03-14T04:33:18.8576860Z         In distributed mode, calling the :meth:`set_epoch` method at
2025-03-14T04:33:18.8577451Z         the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2025-03-14T04:33:18.8578100Z         is necessary to make shuffling work properly across multiple epochs. Otherwise,
2025-03-14T04:33:18.8578619Z         the same ordering will be always used.
2025-03-14T04:33:18.8578858Z 
2025-03-14T04:33:18.8578970Z     Example::
2025-03-14T04:33:18.8579107Z 
2025-03-14T04:33:18.8579228Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:18.8579653Z         >>> sampler = DistributedSampler(dataset) if is_distributed else None
2025-03-14T04:33:18.8580200Z         >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2025-03-14T04:33:18.8580630Z         ...                     sampler=sampler)
2025-03-14T04:33:18.8581024Z         >>> for epoch in range(start_epoch, n_epochs):
2025-03-14T04:33:18.8581404Z         ...     if is_distributed:
2025-03-14T04:33:18.8581744Z         ...         sampler.set_epoch(epoch)
2025-03-14T04:33:18.8582097Z         ...     train(loader)
2025-03-14T04:33:18.8582393Z     
2025-03-14T04:33:18.8582790Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:18.8583215Z 
2025-03-14T04:33:19.0415354Z gathering tests
2025-03-14T04:33:19.0426064Z running 709 test(s)
2025-03-14T04:33:19.0440448Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1077 <- wrt source file
2025-03-14T04:33:19.0447991Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0
2025-03-14T04:33:19.0452592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1113 <- wrt source file
2025-03-14T04:33:19.0453851Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0
2025-03-14T04:33:19.0455129Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1182 <- wrt source file
2025-03-14T04:33:19.0456422Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0
2025-03-14T04:33:19.0457747Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1231 <- wrt source file
2025-03-14T04:33:19.0459310Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0
2025-03-14T04:33:19.0460649Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1268 <- wrt source file
2025-03-14T04:33:19.0461969Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0
2025-03-14T04:33:19.0463265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1423 <- wrt source file
2025-03-14T04:33:19.0464636Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0
2025-03-14T04:33:19.0465881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2523 <- wrt source file
2025-03-14T04:33:19.0466988Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0
2025-03-14T04:33:19.0468340Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2785 <- wrt source file
2025-03-14T04:33:19.0469700Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0
2025-03-14T04:33:19.0471090Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file
2025-03-14T04:33:19.0472451Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0
2025-03-14T04:33:19.0473775Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file
2025-03-14T04:33:19.0475140Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0
2025-03-14T04:33:19.0476354Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file
2025-03-14T04:33:19.0477494Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0
2025-03-14T04:33:19.0478660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file
2025-03-14T04:33:19.0479837Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0
2025-03-14T04:33:19.0481086Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file
2025-03-14T04:33:19.1048906Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0
2025-03-14T04:33:19.1050191Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file
2025-03-14T04:33:19.1051509Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0
2025-03-14T04:33:19.1052746Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 672 <- wrt source file
2025-03-14T04:33:19.1057344Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0
2025-03-14T04:33:19.1058779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 729 <- wrt source file
2025-03-14T04:33:19.1074395Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0
2025-03-14T04:33:19.1075814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1347 <- wrt source file
2025-03-14T04:33:19.1195508Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0
2025-03-14T04:33:19.1198929Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1392 <- wrt source file
2025-03-14T04:33:19.1203620Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0
2025-03-14T04:33:19.1204844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1465 <- wrt source file
2025-03-14T04:33:19.1210576Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0
2025-03-14T04:33:19.1211808Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1495 <- wrt source file
2025-03-14T04:33:19.1216001Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0
2025-03-14T04:33:19.1217327Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file
2025-03-14T04:33:19.1235419Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0
2025-03-14T04:33:19.1236756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file
2025-03-14T04:33:19.1241568Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0
2025-03-14T04:33:19.1242898Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file
2025-03-14T04:33:19.1244394Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0
2025-03-14T04:33:19.1245660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 193 <- wrt source file
2025-03-14T04:33:19.1256142Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0
2025-03-14T04:33:19.1257313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 307 <- wrt source file
2025-03-14T04:33:19.1304939Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0
2025-03-14T04:33:19.1306151Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1041 <- wrt source file
2025-03-14T04:33:19.1316030Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0
2025-03-14T04:33:19.1317242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1316 <- wrt source file
2025-03-14T04:33:19.1326178Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0
2025-03-14T04:33:19.1327350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1400 <- wrt source file
2025-03-14T04:33:19.1332909Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0
2025-03-14T04:33:19.1334067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1434 <- wrt source file
2025-03-14T04:33:19.1341244Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0
2025-03-14T04:33:19.1342368Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1485 <- wrt source file
2025-03-14T04:33:19.1354540Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0
2025-03-14T04:33:19.1355663Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1526 <- wrt source file
2025-03-14T04:33:19.1370165Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0
2025-03-14T04:33:19.1371305Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1562 <- wrt source file
2025-03-14T04:33:19.1386132Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0
2025-03-14T04:33:19.1387344Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1600 <- wrt source file
2025-03-14T04:33:19.1406487Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0
2025-03-14T04:33:19.1407596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1773 <- wrt source file
2025-03-14T04:33:19.1437287Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0
2025-03-14T04:33:19.1438432Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1940 <- wrt source file
2025-03-14T04:33:19.1463348Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0
2025-03-14T04:33:19.1464656Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2040 <- wrt source file
2025-03-14T04:33:19.1465978Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0
2025-03-14T04:33:19.1467169Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2140 <- wrt source file
2025-03-14T04:33:19.1468442Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0
2025-03-14T04:33:19.1469683Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 468 <- wrt source file
2025-03-14T04:33:19.1470881Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0
2025-03-14T04:33:19.1471903Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 528 <- wrt source file
2025-03-14T04:33:19.1472937Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0
2025-03-14T04:33:19.1474037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 151 <- wrt source file
2025-03-14T04:33:19.1475209Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0
2025-03-14T04:33:19.1476433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 251 <- wrt source file
2025-03-14T04:33:19.1487584Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0
2025-03-14T04:33:19.1488823Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 306 <- wrt source file
2025-03-14T04:33:19.1491524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0
2025-03-14T04:33:19.1492645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 499 <- wrt source file
2025-03-14T04:33:19.1507162Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0
2025-03-14T04:33:19.1508230Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 605 <- wrt source file
2025-03-14T04:33:19.1523309Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0
2025-03-14T04:33:19.1524508Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 786 <- wrt source file
2025-03-14T04:33:19.1525749Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0
2025-03-14T04:33:19.1526921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0, line 854 <- wrt source file
2025-03-14T04:33:19.1528217Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0
2025-03-14T04:33:19.1529430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1190 <- wrt source file
2025-03-14T04:33:19.1629496Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0
2025-03-14T04:33:19.1630700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1279 <- wrt source file
2025-03-14T04:33:19.1790129Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0
2025-03-14T04:33:19.1791331Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 112 <- wrt source file
2025-03-14T04:33:19.1795969Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0
2025-03-14T04:33:19.1797204Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file
2025-03-14T04:33:19.1827646Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0
2025-03-14T04:33:19.1829016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1571 <- wrt source file
2025-03-14T04:33:19.1830613Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0
2025-03-14T04:33:19.1831845Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1706 <- wrt source file
2025-03-14T04:33:19.1833237Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0
2025-03-14T04:33:19.1834508Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1954 <- wrt source file
2025-03-14T04:33:19.1862269Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0
2025-03-14T04:33:19.1863521Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1973 <- wrt source file
2025-03-14T04:33:19.1868835Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0
2025-03-14T04:33:19.1870202Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file
2025-03-14T04:33:19.1871608Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0
2025-03-14T04:33:19.1872874Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file
2025-03-14T04:33:19.1874120Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0
2025-03-14T04:33:19.1875378Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file
2025-03-14T04:33:19.1876723Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0
2025-03-14T04:33:19.1877954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file
2025-03-14T04:33:19.1879303Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0
2025-03-14T04:33:19.1880584Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file
2025-03-14T04:33:19.1881847Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0
2025-03-14T04:33:19.1883013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 948 <- wrt source file
2025-03-14T04:33:19.1884161Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0
2025-03-14T04:33:19.1885312Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file
2025-03-14T04:33:19.1886509Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0
2025-03-14T04:33:19.1887727Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 306 <- wrt source file
2025-03-14T04:33:19.1889008Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0
2025-03-14T04:33:19.1890287Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 343 <- wrt source file
2025-03-14T04:33:19.1891630Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0
2025-03-14T04:33:19.1893016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1793 <- wrt source file
2025-03-14T04:33:19.1895184Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0
2025-03-14T04:33:19.1896562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 79 <- wrt source file
2025-03-14T04:33:19.1900059Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0
2025-03-14T04:33:19.1901432Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 117 <- wrt source file
2025-03-14T04:33:19.1902706Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0
2025-03-14T04:33:19.1904008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 171 <- wrt source file
2025-03-14T04:33:19.2380795Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0
2025-03-14T04:33:19.2382050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 357 <- wrt source file
2025-03-14T04:33:19.2383440Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0
2025-03-14T04:33:19.2384696Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 389 <- wrt source file
2025-03-14T04:33:19.2386078Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0
2025-03-14T04:33:19.2387385Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 410 <- wrt source file
2025-03-14T04:33:19.2388845Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0
2025-03-14T04:33:19.2390505Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 428 <- wrt source file
2025-03-14T04:33:19.2391946Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0
2025-03-14T04:33:19.2393310Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 443 <- wrt source file
2025-03-14T04:33:19.2394703Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0
2025-03-14T04:33:19.2396097Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 458 <- wrt source file
2025-03-14T04:33:19.2397524Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0
2025-03-14T04:33:19.2398846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 406 <- wrt source file
2025-03-14T04:33:19.2400191Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0
2025-03-14T04:33:19.2401384Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 488 <- wrt source file
2025-03-14T04:33:19.2402794Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0
2025-03-14T04:33:19.2404112Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 586 <- wrt source file
2025-03-14T04:33:19.2405498Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0
2025-03-14T04:33:19.2406861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 200 <- wrt source file
2025-03-14T04:33:19.2408325Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0
2025-03-14T04:33:19.2409750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 262 <- wrt source file
2025-03-14T04:33:19.2411188Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0
2025-03-14T04:33:19.2412616Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file
2025-03-14T04:33:19.2413949Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0
2025-03-14T04:33:19.2415166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file
2025-03-14T04:33:19.2416439Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0
2025-03-14T04:33:19.2417739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file
2025-03-14T04:33:19.2419169Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0
2025-03-14T04:33:19.2420440Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 61 <- wrt source file
2025-03-14T04:33:19.2435316Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0
2025-03-14T04:33:19.2436614Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file
2025-03-14T04:33:19.2440660Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0
2025-03-14T04:33:19.2442843Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file
2025-03-14T04:33:19.2489631Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0
2025-03-14T04:33:19.2491938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 405 <- wrt source file
2025-03-14T04:33:19.2508836Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0
2025-03-14T04:33:19.2511163Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 479 <- wrt source file
2025-03-14T04:33:19.2525486Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0
2025-03-14T04:33:19.2527871Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file
2025-03-14T04:33:19.2534789Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0
2025-03-14T04:33:19.2537279Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file
2025-03-14T04:33:19.2590024Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0
2025-03-14T04:33:19.2592427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 317 <- wrt source file
2025-03-14T04:33:19.2594884Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0
2025-03-14T04:33:19.2597436Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 321 <- wrt source file
2025-03-14T04:33:19.2600217Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0
2025-03-14T04:33:19.2602960Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1233 <- wrt source file
2025-03-14T04:33:19.2605590Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0
2025-03-14T04:33:19.2608050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 886 <- wrt source file
2025-03-14T04:33:19.2860793Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0
2025-03-14T04:33:19.2863005Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0, line 323 <- wrt source file
2025-03-14T04:33:19.2865116Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0
2025-03-14T04:33:19.2867473Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file
2025-03-14T04:33:19.2870262Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0
2025-03-14T04:33:19.2872680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 232 <- wrt source file
2025-03-14T04:33:19.2899697Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0
2025-03-14T04:33:19.2902009Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 474 <- wrt source file
2025-03-14T04:33:19.2957142Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0
2025-03-14T04:33:19.2959546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file
2025-03-14T04:33:19.3421840Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0
2025-03-14T04:33:19.3424168Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file
2025-03-14T04:33:19.3488556Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0
2025-03-14T04:33:19.3491138Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file
2025-03-14T04:33:19.3506241Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0
2025-03-14T04:33:19.3508668Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file
2025-03-14T04:33:19.3511200Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0
2025-03-14T04:33:19.3513615Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file
2025-03-14T04:33:19.3658728Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0
2025-03-14T04:33:19.3661160Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file
2025-03-14T04:33:19.3663668Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0
2025-03-14T04:33:19.3666158Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file
2025-03-14T04:33:19.3668448Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0
2025-03-14T04:33:19.3671090Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 115 <- wrt source file
2025-03-14T04:33:19.3674142Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0
2025-03-14T04:33:19.3676917Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 128 <- wrt source file
2025-03-14T04:33:19.3679618Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0
2025-03-14T04:33:19.3682370Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 270 <- wrt source file
2025-03-14T04:33:19.3685171Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0
2025-03-14T04:33:19.3687588Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 110 <- wrt source file
2025-03-14T04:33:19.3689799Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0
2025-03-14T04:33:19.3692134Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0, line 80 <- wrt source file
2025-03-14T04:33:19.3694696Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0
2025-03-14T04:33:19.3697013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 94 <- wrt source file
2025-03-14T04:33:19.3699212Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0
2025-03-14T04:33:19.3701665Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1351 <- wrt source file
2025-03-14T04:33:19.3704527Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0
2025-03-14T04:33:19.3707033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 99 <- wrt source file
2025-03-14T04:33:19.4002346Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0
2025-03-14T04:33:19.4004770Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 230 <- wrt source file
2025-03-14T04:33:19.4084897Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0
2025-03-14T04:33:19.4087485Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 299 <- wrt source file
2025-03-14T04:33:19.4090114Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0
2025-03-14T04:33:19.4092793Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 405 <- wrt source file
2025-03-14T04:33:19.4162678Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0
2025-03-14T04:33:19.4165250Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 532 <- wrt source file
2025-03-14T04:33:19.4322967Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0
2025-03-14T04:33:19.4325535Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 704 <- wrt source file
2025-03-14T04:33:19.4480188Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0
2025-03-14T04:33:19.4482753Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 790 <- wrt source file
2025-03-14T04:33:19.4485550Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0
2025-03-14T04:33:19.4488121Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 197 <- wrt source file
2025-03-14T04:33:19.4490720Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0
2025-03-14T04:33:19.4493262Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 161 <- wrt source file
2025-03-14T04:33:19.4552082Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0
2025-03-14T04:33:19.4554493Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 51 <- wrt source file
2025-03-14T04:33:19.4558108Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0
2025-03-14T04:33:19.4559767Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 442 <- wrt source file
2025-03-14T04:33:19.4560974Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0
2025-03-14T04:33:19.4562286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file
2025-03-14T04:33:19.4600116Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0
2025-03-14T04:33:19.4601400Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 301 <- wrt source file
2025-03-14T04:33:19.4602735Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0
2025-03-14T04:33:19.4604025Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 992 <- wrt source file
2025-03-14T04:33:19.4649483Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0
2025-03-14T04:33:19.4650800Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1057 <- wrt source file
2025-03-14T04:33:19.4652251Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0
2025-03-14T04:33:19.4653548Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1278 <- wrt source file
2025-03-14T04:33:19.4667767Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0
2025-03-14T04:33:19.4669120Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1344 <- wrt source file
2025-03-14T04:33:19.4671664Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0
2025-03-14T04:33:19.4673038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1407 <- wrt source file
2025-03-14T04:33:19.4675997Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0
2025-03-14T04:33:19.4677343Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1452 <- wrt source file
2025-03-14T04:33:19.4678600Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0
2025-03-14T04:33:19.4680040Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1562 <- wrt source file
2025-03-14T04:33:19.4681781Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0
2025-03-14T04:33:19.4683001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 86 <- wrt source file
2025-03-14T04:33:19.4684230Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0
2025-03-14T04:33:19.4685426Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file
2025-03-14T04:33:19.4686633Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0
2025-03-14T04:33:19.4687969Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file
2025-03-14T04:33:19.4689546Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0
2025-03-14T04:33:19.4691149Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file
2025-03-14T04:33:19.4692827Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0
2025-03-14T04:33:19.4694404Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file
2025-03-14T04:33:19.4695971Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0
2025-03-14T04:33:19.4697532Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file
2025-03-14T04:33:19.4699167Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0
2025-03-14T04:33:19.4700770Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file
2025-03-14T04:33:19.4702345Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0
2025-03-14T04:33:19.4703760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 30 <- wrt source file
2025-03-14T04:33:19.4708495Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0
2025-03-14T04:33:19.4709994Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 410 <- wrt source file
2025-03-14T04:33:19.4739879Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0
2025-03-14T04:33:19.4742115Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file
2025-03-14T04:33:19.4743490Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0
2025-03-14T04:33:19.4744765Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file
2025-03-14T04:33:19.4746065Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0
2025-03-14T04:33:19.4747338Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file
2025-03-14T04:33:19.4748643Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0
2025-03-14T04:33:19.4750113Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file
2025-03-14T04:33:19.4751490Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0
2025-03-14T04:33:19.4752842Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file
2025-03-14T04:33:19.4754233Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0
2025-03-14T04:33:19.4755686Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file
2025-03-14T04:33:19.4757156Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0
2025-03-14T04:33:19.4758543Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file
2025-03-14T04:33:19.4760047Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0
2025-03-14T04:33:19.4761436Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 208 <- wrt source file
2025-03-14T04:33:19.4762855Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0
2025-03-14T04:33:19.4764306Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 294 <- wrt source file
2025-03-14T04:33:19.4765889Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0
2025-03-14T04:33:19.4767388Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 376 <- wrt source file
2025-03-14T04:33:19.4768931Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0
2025-03-14T04:33:19.4770424Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 458 <- wrt source file
2025-03-14T04:33:19.4771955Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0
2025-03-14T04:33:19.4773409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file
2025-03-14T04:33:19.4775838Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0
2025-03-14T04:33:19.4778463Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file
2025-03-14T04:33:19.4781024Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0
2025-03-14T04:33:19.4783513Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file
2025-03-14T04:33:19.4786288Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0
2025-03-14T04:33:19.4789243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file
2025-03-14T04:33:19.4792351Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0
2025-03-14T04:33:19.4795305Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file
2025-03-14T04:33:19.4798388Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0
2025-03-14T04:33:19.4801502Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file
2025-03-14T04:33:19.4804296Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0
2025-03-14T04:33:19.4806820Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file
2025-03-14T04:33:19.4809378Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0
2025-03-14T04:33:19.4811805Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 505 <- wrt source file
2025-03-14T04:33:19.4814226Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0
2025-03-14T04:33:19.4816609Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 634 <- wrt source file
2025-03-14T04:33:19.4819044Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0
2025-03-14T04:33:19.4821595Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 890 <- wrt source file
2025-03-14T04:33:19.4824244Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0
2025-03-14T04:33:19.4826839Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1012 <- wrt source file
2025-03-14T04:33:19.4829484Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0
2025-03-14T04:33:19.4832089Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1138 <- wrt source file
2025-03-14T04:33:19.4834752Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0
2025-03-14T04:33:19.4837378Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file
2025-03-14T04:33:19.4840188Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0
2025-03-14T04:33:19.4842891Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 275 <- wrt source file
2025-03-14T04:33:19.4845664Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0
2025-03-14T04:33:19.4848470Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 23 <- wrt source file
2025-03-14T04:33:19.4851555Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0
2025-03-14T04:33:19.4854409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 176 <- wrt source file
2025-03-14T04:33:19.4857286Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0
2025-03-14T04:33:19.4859889Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file
2025-03-14T04:33:19.4862429Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0
2025-03-14T04:33:19.4865445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file
2025-03-14T04:33:19.4868985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0
2025-03-14T04:33:19.4872431Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file
2025-03-14T04:33:19.4876001Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0
2025-03-14T04:33:19.4879398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file
2025-03-14T04:33:19.4882746Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0
2025-03-14T04:33:19.4885648Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file
2025-03-14T04:33:19.4888314Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0
2025-03-14T04:33:19.4890993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file
2025-03-14T04:33:19.4893793Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0
2025-03-14T04:33:19.4896396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 176 <- wrt source file
2025-03-14T04:33:19.4898917Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0
2025-03-14T04:33:19.4901519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file
2025-03-14T04:33:19.4904214Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0
2025-03-14T04:33:19.4906896Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file
2025-03-14T04:33:19.4909701Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0
2025-03-14T04:33:19.4912416Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file
2025-03-14T04:33:19.4915159Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0
2025-03-14T04:33:19.4917951Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file
2025-03-14T04:33:19.4920894Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0
2025-03-14T04:33:19.4923572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 108 <- wrt source file
2025-03-14T04:33:19.4925986Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0
2025-03-14T04:33:19.4928452Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 130 <- wrt source file
2025-03-14T04:33:19.4931084Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0
2025-03-14T04:33:19.4933537Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file
2025-03-14T04:33:19.4935938Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0
2025-03-14T04:33:19.4938341Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file
2025-03-14T04:33:19.4940864Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0
2025-03-14T04:33:19.4943346Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file
2025-03-14T04:33:19.4945893Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0
2025-03-14T04:33:19.4948362Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 598 <- wrt source file
2025-03-14T04:33:19.4950975Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0
2025-03-14T04:33:19.4953528Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 658 <- wrt source file
2025-03-14T04:33:19.4956247Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0
2025-03-14T04:33:19.4959223Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 710 <- wrt source file
2025-03-14T04:33:19.4962179Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0
2025-03-14T04:33:19.4964884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 47 <- wrt source file
2025-03-14T04:33:19.4967451Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0
2025-03-14T04:33:19.4969998Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 125 <- wrt source file
2025-03-14T04:33:19.4972629Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0
2025-03-14T04:33:19.4975181Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 222 <- wrt source file
2025-03-14T04:33:19.4977733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0
2025-03-14T04:33:19.4980265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file
2025-03-14T04:33:19.4982790Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0
2025-03-14T04:33:19.4985228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 517 <- wrt source file
2025-03-14T04:33:19.4987729Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0
2025-03-14T04:33:19.4990216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 539 <- wrt source file
2025-03-14T04:33:19.4992769Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0
2025-03-14T04:33:19.4995253Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 553 <- wrt source file
2025-03-14T04:33:19.4997791Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0
2025-03-14T04:33:19.5000374Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 575 <- wrt source file
2025-03-14T04:33:19.5002874Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0
2025-03-14T04:33:19.5005294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 702 <- wrt source file
2025-03-14T04:33:19.5007744Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0
2025-03-14T04:33:19.5010427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file
2025-03-14T04:33:19.5013383Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0
2025-03-14T04:33:19.5016265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file
2025-03-14T04:33:19.5019107Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0
2025-03-14T04:33:19.5021941Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0, line 102 <- wrt source file
2025-03-14T04:33:19.5024888Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0
2025-03-14T04:33:19.5027773Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0, line 148 <- wrt source file
2025-03-14T04:33:19.5030700Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0
2025-03-14T04:33:19.5033520Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file
2025-03-14T04:33:19.5036366Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0
2025-03-14T04:33:19.5039339Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 430 <- wrt source file
2025-03-14T04:33:19.5042381Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0
2025-03-14T04:33:19.5045026Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file
2025-03-14T04:33:19.5047435Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0
2025-03-14T04:33:19.5049843Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 83 <- wrt source file
2025-03-14T04:33:19.5052106Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0
2025-03-14T04:33:19.5054350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 153 <- wrt source file
2025-03-14T04:33:19.5056666Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0
2025-03-14T04:33:19.5058985Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 189 <- wrt source file
2025-03-14T04:33:19.5061269Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0
2025-03-14T04:33:19.5063691Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 66 <- wrt source file
2025-03-14T04:33:19.5066333Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0
2025-03-14T04:33:19.5068918Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 109 <- wrt source file
2025-03-14T04:33:19.5071546Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0
2025-03-14T04:33:19.5074061Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 160 <- wrt source file
2025-03-14T04:33:19.5076622Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0
2025-03-14T04:33:19.5079255Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 207 <- wrt source file
2025-03-14T04:33:19.5082011Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0
2025-03-14T04:33:19.5084715Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 236 <- wrt source file
2025-03-14T04:33:19.5087443Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0
2025-03-14T04:33:19.5089870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file
2025-03-14T04:33:19.5092091Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0
2025-03-14T04:33:19.5094248Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file
2025-03-14T04:33:19.5096430Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0
2025-03-14T04:33:19.5098667Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file
2025-03-14T04:33:19.5100868Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0
2025-03-14T04:33:19.5103058Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file
2025-03-14T04:33:19.5105333Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0
2025-03-14T04:33:19.5107540Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 884 <- wrt source file
2025-03-14T04:33:19.5109788Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0
2025-03-14T04:33:19.5111972Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1000 <- wrt source file
2025-03-14T04:33:19.5114158Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0
2025-03-14T04:33:19.5116350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1099 <- wrt source file
2025-03-14T04:33:19.5118527Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0
2025-03-14T04:33:19.5120674Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file
2025-03-14T04:33:19.5122864Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0
2025-03-14T04:33:19.5125074Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file
2025-03-14T04:33:19.5127345Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0
2025-03-14T04:33:19.5129621Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file
2025-03-14T04:33:19.5132110Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0
2025-03-14T04:33:19.5134438Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 232 <- wrt source file
2025-03-14T04:33:19.5136777Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0
2025-03-14T04:33:19.5138981Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file
2025-03-14T04:33:19.5141150Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0
2025-03-14T04:33:19.5143368Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 110 <- wrt source file
2025-03-14T04:33:19.5145718Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0
2025-03-14T04:33:19.5148052Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 147 <- wrt source file
2025-03-14T04:33:19.5150624Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0
2025-03-14T04:33:19.5153034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 271 <- wrt source file
2025-03-14T04:33:19.5155437Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0
2025-03-14T04:33:19.5157677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 336 <- wrt source file
2025-03-14T04:33:19.5159924Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0
2025-03-14T04:33:19.5162261Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 393 <- wrt source file
2025-03-14T04:33:19.5164784Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0
2025-03-14T04:33:19.5167218Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 470 <- wrt source file
2025-03-14T04:33:19.5169685Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0
2025-03-14T04:33:19.5172235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 736 <- wrt source file
2025-03-14T04:33:19.5174837Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0
2025-03-14T04:33:19.5177191Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 178 <- wrt source file
2025-03-14T04:33:19.5179387Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0
2025-03-14T04:33:19.5181635Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0, line 733 <- wrt source file
2025-03-14T04:33:19.5184002Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0
2025-03-14T04:33:19.5186245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 867 <- wrt source file
2025-03-14T04:33:19.5188502Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0
2025-03-14T04:33:19.5190701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 940 <- wrt source file
2025-03-14T04:33:19.5192933Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0
2025-03-14T04:33:19.5195110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0, line 42 <- wrt source file
2025-03-14T04:33:19.5197505Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0
2025-03-14T04:33:19.5200077Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0, line 58 <- wrt source file
2025-03-14T04:33:19.5202693Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0
2025-03-14T04:33:19.5204978Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file
2025-03-14T04:33:19.5206985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0
2025-03-14T04:33:19.5209090Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file
2025-03-14T04:33:19.5211450Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0
2025-03-14T04:33:19.5213668Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file
2025-03-14T04:33:19.5215933Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1
2025-03-14T04:33:19.5218143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file
2025-03-14T04:33:19.5220387Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2
2025-03-14T04:33:19.5222717Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file
2025-03-14T04:33:19.5225224Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0
2025-03-14T04:33:19.5227495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file
2025-03-14T04:33:19.5229655Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0
2025-03-14T04:33:19.5231891Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 415 <- wrt source file
2025-03-14T04:33:19.5234288Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0
2025-03-14T04:33:19.5236769Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 931 <- wrt source file
2025-03-14T04:33:19.5239482Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0
2025-03-14T04:33:19.5242041Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1013 <- wrt source file
2025-03-14T04:33:19.5244538Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0
2025-03-14T04:33:19.5247153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2523 <- wrt source file
2025-03-14T04:33:19.5249985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0
2025-03-14T04:33:19.5252605Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3035 <- wrt source file
2025-03-14T04:33:19.5255241Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0
2025-03-14T04:33:19.5257812Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3256 <- wrt source file
2025-03-14T04:33:19.5260426Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0
2025-03-14T04:33:19.5263000Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3354 <- wrt source file
2025-03-14T04:33:19.5265609Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0
2025-03-14T04:33:19.5268290Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3464 <- wrt source file
2025-03-14T04:33:19.5271043Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0
2025-03-14T04:33:19.5273699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3583 <- wrt source file
2025-03-14T04:33:19.5276377Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0
2025-03-14T04:33:19.5279016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3792 <- wrt source file
2025-03-14T04:33:19.5281790Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0
2025-03-14T04:33:19.5284463Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3934 <- wrt source file
2025-03-14T04:33:19.5287234Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0
2025-03-14T04:33:19.5289746Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0, line 4040 <- wrt source file
2025-03-14T04:33:19.5292177Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0
2025-03-14T04:33:19.5294580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 4126 <- wrt source file
2025-03-14T04:33:19.5297034Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter:0
2025-03-14T04:33:19.5299576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 4265 <- wrt source file
2025-03-14T04:33:19.5302301Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0
2025-03-14T04:33:19.5304996Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4732 <- wrt source file
2025-03-14T04:33:19.5307654Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0
2025-03-14T04:33:19.5310209Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0, line 5310 <- wrt source file
2025-03-14T04:33:19.5312785Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0
2025-03-14T04:33:19.5315453Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0, line 5412 <- wrt source file
2025-03-14T04:33:19.5318319Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0
2025-03-14T04:33:19.5320780Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/run.py::__doc__:0, line 57 <- wrt source file
2025-03-14T04:33:19.5322912Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/run.py::__doc__:0
2025-03-14T04:33:19.5325174Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file
2025-03-14T04:33:19.5327697Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0
2025-03-14T04:33:19.5330303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 53 <- wrt source file
2025-03-14T04:33:19.5333144Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0
2025-03-14T04:33:19.5335751Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0, line 66 <- wrt source file
2025-03-14T04:33:19.5338288Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0
2025-03-14T04:33:19.5340799Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file
2025-03-14T04:33:19.5343384Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0
2025-03-14T04:33:19.5346187Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 31 <- wrt source file
2025-03-14T04:33:19.5349381Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0
2025-03-14T04:33:19.5352328Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file
2025-03-14T04:33:19.5355262Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0
2025-03-14T04:33:19.5358122Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file
2025-03-14T04:33:19.5361131Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0
2025-03-14T04:33:19.5363934Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0, line 55 <- wrt source file
2025-03-14T04:33:19.5366568Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0
2025-03-14T04:33:19.5369043Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/join.py::Join:0, line 141 <- wrt source file
2025-03-14T04:33:19.5371422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/join.py::Join:0
2025-03-14T04:33:19.5374076Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0, line 107 <- wrt source file
2025-03-14T04:33:19.5377122Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0
2025-03-14T04:33:19.5380060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0, line 23 <- wrt source file
2025-03-14T04:33:19.5383030Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0
2025-03-14T04:33:19.5386014Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0, line 49 <- wrt source file
2025-03-14T04:33:19.5389089Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0
2025-03-14T04:33:19.5392104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0, line 104 <- wrt source file
2025-03-14T04:33:19.5395208Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0
2025-03-14T04:33:19.5398241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0, line 125 <- wrt source file
2025-03-14T04:33:19.5401373Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0
2025-03-14T04:33:19.5404428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0, line 143 <- wrt source file
2025-03-14T04:33:19.5407651Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0
2025-03-14T04:33:19.5410756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0, line 182 <- wrt source file
2025-03-14T04:33:19.5413912Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0
2025-03-14T04:33:19.5416998Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0, line 707 <- wrt source file
2025-03-14T04:33:19.5420168Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0
2025-03-14T04:33:19.5423430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0, line 64 <- wrt source file
2025-03-14T04:33:19.5426836Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0
2025-03-14T04:33:19.5430188Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0, line 145 <- wrt source file
2025-03-14T04:33:19.5433630Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0
2025-03-14T04:33:19.5436698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1401 <- wrt source file
2025-03-14T04:33:19.5439633Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0
2025-03-14T04:33:19.5456815Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1460 <- wrt source file
2025-03-14T04:33:19.5459834Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0
2025-03-14T04:33:19.5463020Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0, line 231 <- wrt source file
2025-03-14T04:33:19.5466077Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0
2025-03-14T04:33:19.5468923Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0, line 141 <- wrt source file
2025-03-14T04:33:19.5471708Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0
2025-03-14T04:33:19.5474250Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::StateDictType:0, line 262 <- wrt source file
2025-03-14T04:33:19.5476666Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::StateDictType:0
2025-03-14T04:33:19.5479488Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0, line 130 <- wrt source file
2025-03-14T04:33:19.5482670Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0
2025-03-14T04:33:19.5486000Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0, line 1495 <- wrt source file
2025-03-14T04:33:19.5489622Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0
2025-03-14T04:33:19.5493218Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0, line 1615 <- wrt source file
2025-03-14T04:33:19.5497231Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0
2025-03-14T04:33:19.5501600Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0, line 1700 <- wrt source file
2025-03-14T04:33:19.5505725Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0
2025-03-14T04:33:19.5508994Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0, line 54 <- wrt source file
2025-03-14T04:33:19.5511975Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0
2025-03-14T04:33:19.5514682Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 224 <- wrt source file
2025-03-14T04:33:19.5517227Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0
2025-03-14T04:33:19.5519731Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file
2025-03-14T04:33:19.5522297Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0
2025-03-14T04:33:19.5525164Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 43 <- wrt source file
2025-03-14T04:33:19.5528366Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0
2025-03-14T04:33:19.5531462Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 114 <- wrt source file
2025-03-14T04:33:19.5534610Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0
2025-03-14T04:33:19.5537490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 44 <- wrt source file
2025-03-14T04:33:19.5540217Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0
2025-03-14T04:33:19.5542870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 37 <- wrt source file
2025-03-14T04:33:19.5545617Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0
2025-03-14T04:33:19.5548155Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0, line 333 <- wrt source file
2025-03-14T04:33:19.5550766Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0
2025-03-14T04:33:19.5553460Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0, line 82 <- wrt source file
2025-03-14T04:33:19.5556460Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0
2025-03-14T04:33:19.5559433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0, line 101 <- wrt source file
2025-03-14T04:33:19.5562486Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0
2025-03-14T04:33:19.5565020Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 175 <- wrt source file
2025-03-14T04:33:19.5567279Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0
2025-03-14T04:33:19.5569505Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 346 <- wrt source file
2025-03-14T04:33:19.5571768Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::shutdown:0
2025-03-14T04:33:19.5573970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::remote:0, line 605 <- wrt source file
2025-03-14T04:33:19.5576189Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::remote:0
2025-03-14T04:33:19.5578375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 785 <- wrt source file
2025-03-14T04:33:19.5580625Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_sync:0
2025-03-14T04:33:19.5582832Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 877 <- wrt source file
2025-03-14T04:33:19.5585204Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_async:0
2025-03-14T04:33:19.5587508Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0, line 813 <- wrt source file
2025-03-14T04:33:19.5589951Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0
2025-03-14T04:33:19.5592640Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 251 <- wrt source file
2025-03-14T04:33:19.5595643Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0
2025-03-14T04:33:19.5598444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0, line 235 <- wrt source file
2025-03-14T04:33:19.5601242Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0
2025-03-14T04:33:19.5604023Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 57 <- wrt source file
2025-03-14T04:33:19.5606781Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0
2025-03-14T04:33:19.5609526Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0, line 88 <- wrt source file
2025-03-14T04:33:19.5612373Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0
2025-03-14T04:33:19.5615095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0, line 55 <- wrt source file
2025-03-14T04:33:19.5617776Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0
2025-03-14T04:33:19.5620486Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0, line 63 <- wrt source file
2025-03-14T04:33:19.5623249Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0
2025-03-14T04:33:19.5625966Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0, line 189 <- wrt source file
2025-03-14T04:33:19.5628746Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0
2025-03-14T04:33:19.5631474Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0, line 333 <- wrt source file
2025-03-14T04:33:19.5634259Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0
2025-03-14T04:33:19.5636769Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 28 <- wrt source file
2025-03-14T04:33:19.5639188Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0
2025-03-14T04:33:19.5641415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 19 <- wrt source file
2025-03-14T04:33:19.5643634Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0
2025-03-14T04:33:19.5645881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 29 <- wrt source file
2025-03-14T04:33:19.5648234Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0
2025-03-14T04:33:19.5650758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file
2025-03-14T04:33:19.5653250Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0
2025-03-14T04:33:19.5655564Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 22 <- wrt source file
2025-03-14T04:33:19.5657813Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0
2025-03-14T04:33:19.5659963Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0, line 16 <- wrt source file
2025-03-14T04:33:19.5662220Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0
2025-03-14T04:33:19.5664521Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0, line 164 <- wrt source file
2025-03-14T04:33:19.5667010Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0
2025-03-14T04:33:19.5669538Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 185 <- wrt source file
2025-03-14T04:33:19.5672206Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0
2025-03-14T04:33:19.5674909Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 34 <- wrt source file
2025-03-14T04:33:19.5677767Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0
2025-03-14T04:33:19.5680411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 40 <- wrt source file
2025-03-14T04:33:19.5682909Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0
2025-03-14T04:33:19.5685303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0, line 18 <- wrt source file
2025-03-14T04:33:19.5687793Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0
2025-03-14T04:33:19.5690329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 19 <- wrt source file
2025-03-14T04:33:19.5692956Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0
2025-03-14T04:33:19.5695299Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0, line 22 <- wrt source file
2025-03-14T04:33:19.5697502Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0
2025-03-14T04:33:19.5699757Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0, line 34 <- wrt source file
2025-03-14T04:33:19.5702186Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0
2025-03-14T04:33:19.5704491Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 22 <- wrt source file
2025-03-14T04:33:19.5706740Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0
2025-03-14T04:33:19.5709037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 23 <- wrt source file
2025-03-14T04:33:19.5711463Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0
2025-03-14T04:33:19.5713840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file
2025-03-14T04:33:19.5716275Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0
2025-03-14T04:33:19.5718699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0, line 23 <- wrt source file
2025-03-14T04:33:19.5721298Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0
2025-03-14T04:33:19.5723759Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 22 <- wrt source file
2025-03-14T04:33:19.5726290Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0
2025-03-14T04:33:19.5728760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 28 <- wrt source file
2025-03-14T04:33:19.5731244Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0
2025-03-14T04:33:19.5733593Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0, line 18 <- wrt source file
2025-03-14T04:33:19.5735888Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0
2025-03-14T04:33:19.5738268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file
2025-03-14T04:33:19.5740748Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0
2025-03-14T04:33:19.5743119Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 21 <- wrt source file
2025-03-14T04:33:19.5745497Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0
2025-03-14T04:33:19.5747959Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 26 <- wrt source file
2025-03-14T04:33:19.5750754Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0
2025-03-14T04:33:19.5753252Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 36 <- wrt source file
2025-03-14T04:33:19.5755730Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0
2025-03-14T04:33:19.5758422Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 102 <- wrt source file
2025-03-14T04:33:19.5761345Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0
2025-03-14T04:33:19.5763812Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0, line 21 <- wrt source file
2025-03-14T04:33:19.5766076Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0
2025-03-14T04:33:19.5768521Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 32 <- wrt source file
2025-03-14T04:33:19.5771276Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0
2025-03-14T04:33:19.5773714Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file
2025-03-14T04:33:19.5775972Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0
2025-03-14T04:33:19.5778286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0, line 23 <- wrt source file
2025-03-14T04:33:19.5780577Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0
2025-03-14T04:33:19.5782850Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0, line 21 <- wrt source file
2025-03-14T04:33:19.5785188Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0
2025-03-14T04:33:19.5787578Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1046 <- wrt source file
2025-03-14T04:33:19.5790090Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0
2025-03-14T04:33:19.5792569Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1152 <- wrt source file
2025-03-14T04:33:19.5795167Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0
2025-03-14T04:33:19.5797885Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1226 <- wrt source file
2025-03-14T04:33:19.5800872Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0
2025-03-14T04:33:19.5803406Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0, line 19 <- wrt source file
2025-03-14T04:33:19.5805704Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0
2025-03-14T04:33:19.5807966Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0, line 109 <- wrt source file
2025-03-14T04:33:19.5810307Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0
2025-03-14T04:33:19.5812596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0, line 116 <- wrt source file
2025-03-14T04:33:19.5814942Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0
2025-03-14T04:33:19.5817265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0, line 20 <- wrt source file
2025-03-14T04:33:19.5819591Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0
2025-03-14T04:33:19.5821840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0, line 39 <- wrt source file
2025-03-14T04:33:19.5824131Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0
2025-03-14T04:33:19.5826487Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 611 <- wrt source file
2025-03-14T04:33:19.5828983Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0
2025-03-14T04:33:19.5831189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0, line 101 <- wrt source file
2025-03-14T04:33:19.5833270Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0
2025-03-14T04:33:19.5835520Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1781 <- wrt source file
2025-03-14T04:33:19.5837887Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0
2025-03-14T04:33:19.5840237Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1855 <- wrt source file
2025-03-14T04:33:19.5842528Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0
2025-03-14T04:33:19.5844745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0, line 48 <- wrt source file
2025-03-14T04:33:19.5846975Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0
2025-03-14T04:33:19.5849408Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0, line 464 <- wrt source file
2025-03-14T04:33:19.5851640Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0
2025-03-14T04:33:19.5854003Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0, line 125 <- wrt source file
2025-03-14T04:33:19.5856426Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0
2025-03-14T04:33:19.5858663Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file
2025-03-14T04:33:19.5860842Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::TensorType:0
2025-03-14T04:33:19.5863009Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0, line 65 <- wrt source file
2025-03-14T04:33:19.5865225Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0
2025-03-14T04:33:19.5867418Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- wrt source file
2025-03-14T04:33:19.5869661Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0
2025-03-14T04:33:19.5872151Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0, line 96 <- wrt source file
2025-03-14T04:33:19.5875070Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0
2025-03-14T04:33:19.5877668Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/core.py::reify:0, line 58 <- wrt source file
2025-03-14T04:33:19.5880244Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/core.py::reify:0
2025-03-14T04:33:19.5882820Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0, line 48 <- wrt source file
2025-03-14T04:33:19.5885538Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0
2025-03-14T04:33:19.5888141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unifiable:0, line 11 <- wrt source file
2025-03-14T04:33:19.5890740Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unifiable:0
2025-03-14T04:33:19.5893451Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::reify_object:0, line 37 <- wrt source file
2025-03-14T04:33:19.5896102Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::reify_object:0
2025-03-14T04:33:19.5898691Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unify_object:0, line 93 <- wrt source file
2025-03-14T04:33:19.5901342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unify_object:0
2025-03-14T04:33:19.5904019Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0, line 37 <- wrt source file
2025-03-14T04:33:19.5906817Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0
2025-03-14T04:33:19.5909604Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0, line 64 <- wrt source file
2025-03-14T04:33:19.5912539Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0
2025-03-14T04:33:19.5915352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0, line 90 <- wrt source file
2025-03-14T04:33:19.5918168Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0
2025-03-14T04:33:19.5920982Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0, line 106 <- wrt source file
2025-03-14T04:33:19.5923810Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0
2025-03-14T04:33:19.5926583Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0, line 122 <- wrt source file
2025-03-14T04:33:19.5929437Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0
2025-03-14T04:33:19.5932242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0, line 138 <- wrt source file
2025-03-14T04:33:19.5935184Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0
2025-03-14T04:33:19.5938066Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0, line 158 <- wrt source file
2025-03-14T04:33:19.5940970Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0
2025-03-14T04:33:19.5943814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0, line 178 <- wrt source file
2025-03-14T04:33:19.5946720Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0
2025-03-14T04:33:19.5949640Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0, line 204 <- wrt source file
2025-03-14T04:33:19.5952469Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0
2025-03-14T04:33:19.5955314Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0, line 221 <- wrt source file
2025-03-14T04:33:19.5958140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0
2025-03-14T04:33:19.5960963Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 416 <- wrt source file
2025-03-14T04:33:19.5963776Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0
2025-03-14T04:33:19.5966479Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 15 <- wrt source file
2025-03-14T04:33:19.5969205Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0
2025-03-14T04:33:19.5971878Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 42 <- wrt source file
2025-03-14T04:33:19.5974480Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0
2025-03-14T04:33:19.5977066Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 70 <- wrt source file
2025-03-14T04:33:19.5979738Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0
2025-03-14T04:33:19.5982306Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 95 <- wrt source file
2025-03-14T04:33:19.5984850Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0
2025-03-14T04:33:19.5987429Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 67 <- wrt source file
2025-03-14T04:33:19.5990116Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0
2025-03-14T04:33:19.5992906Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 20 <- wrt source file
2025-03-14T04:33:19.5995936Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0
2025-03-14T04:33:19.5999034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 113 <- wrt source file
2025-03-14T04:33:19.6002273Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0
2025-03-14T04:33:19.6005503Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 138 <- wrt source file
2025-03-14T04:33:19.6008923Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0
2025-03-14T04:33:19.6012198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 191 <- wrt source file
2025-03-14T04:33:19.6015522Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0
2025-03-14T04:33:19.6018786Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file
2025-03-14T04:33:19.6022171Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0
2025-03-14T04:33:19.6025429Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file
2025-03-14T04:33:19.6028661Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0
2025-03-14T04:33:19.6031754Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 18 <- wrt source file
2025-03-14T04:33:19.6034899Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0
2025-03-14T04:33:19.6037885Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 41 <- wrt source file
2025-03-14T04:33:19.6040945Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0
2025-03-14T04:33:19.6043941Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 68 <- wrt source file
2025-03-14T04:33:19.6047043Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0
2025-03-14T04:33:19.6050141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file
2025-03-14T04:33:19.6053127Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0
2025-03-14T04:33:19.6056081Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file
2025-03-14T04:33:19.6059164Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0
2025-03-14T04:33:19.6062227Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file
2025-03-14T04:33:19.6065366Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0
2025-03-14T04:33:19.6068391Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 83 <- wrt source file
2025-03-14T04:33:19.6071483Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0
2025-03-14T04:33:19.6074276Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 122 <- wrt source file
2025-03-14T04:33:19.6076984Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0
2025-03-14T04:33:19.6079512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 92 <- wrt source file
2025-03-14T04:33:19.6081810Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0
2025-03-14T04:33:19.6084092Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0, line 85 <- wrt source file
2025-03-14T04:33:19.6086468Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0
2025-03-14T04:33:19.6089294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 51 <- wrt source file
2025-03-14T04:33:19.6092583Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0
2025-03-14T04:33:19.6095493Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file
2025-03-14T04:33:19.6098059Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0
2025-03-14T04:33:19.6100547Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file
2025-03-14T04:33:19.6103082Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0
2025-03-14T04:33:19.6105654Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file
2025-03-14T04:33:19.6108336Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0
2025-03-14T04:33:19.6110862Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file
2025-03-14T04:33:19.6113311Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0
2025-03-14T04:33:19.6115624Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0, line 1529 <- wrt source file
2025-03-14T04:33:19.6117874Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0
2025-03-14T04:33:19.6120311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file
2025-03-14T04:33:19.6122902Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0
2025-03-14T04:33:19.6125528Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 467 <- wrt source file
2025-03-14T04:33:19.6128227Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0
2025-03-14T04:33:19.6130885Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 586 <- wrt source file
2025-03-14T04:33:19.6392983Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0
2025-03-14T04:33:19.6414739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file
2025-03-14T04:33:19.6423780Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0
2025-03-14T04:33:19.6426322Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file
2025-03-14T04:33:19.6432365Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0
2025-03-14T04:33:19.6434545Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file
2025-03-14T04:33:19.6442091Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0
2025-03-14T04:33:19.6444245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3059 <- wrt source file
2025-03-14T04:33:19.6456914Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0
2025-03-14T04:33:19.6459146Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3136 <- wrt source file
2025-03-14T04:33:19.6463192Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0
2025-03-14T04:33:19.6465350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3466 <- wrt source file
2025-03-14T04:33:19.6472044Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0
2025-03-14T04:33:19.6474310Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3538 <- wrt source file
2025-03-14T04:33:19.6478884Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0
2025-03-14T04:33:19.6481386Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3615 <- wrt source file
2025-03-14T04:33:19.6485534Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0
2025-03-14T04:33:19.6487810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5178 <- wrt source file
2025-03-14T04:33:19.6495499Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0
2025-03-14T04:33:19.6497590Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file
2025-03-14T04:33:19.6503076Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0
2025-03-14T04:33:19.6505132Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file
2025-03-14T04:33:19.6507886Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0
2025-03-14T04:33:19.6509925Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file
2025-03-14T04:33:19.6515525Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0
2025-03-14T04:33:19.6517585Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file
2025-03-14T04:33:19.6520372Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0
2025-03-14T04:33:19.6522501Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file
2025-03-14T04:33:19.6552902Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0
2025-03-14T04:33:19.6555621Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file
2025-03-14T04:33:19.6573289Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0
2025-03-14T04:33:19.6575692Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file
2025-03-14T04:33:19.6578109Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0
2025-03-14T04:33:19.6580126Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file
2025-03-14T04:33:19.6582139Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0
2025-03-14T04:33:19.6584212Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file
2025-03-14T04:33:19.6586221Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0
2025-03-14T04:33:19.6588225Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file
2025-03-14T04:33:19.6590315Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0
2025-03-14T04:33:19.6592330Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file
2025-03-14T04:33:19.6594359Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0
2025-03-14T04:33:19.6596307Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file
2025-03-14T04:33:19.6598255Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0
2025-03-14T04:33:19.6600225Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file
2025-03-14T04:33:19.6602181Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0
2025-03-14T04:33:19.6604226Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file
2025-03-14T04:33:19.6606169Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0
2025-03-14T04:33:19.6608079Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file
2025-03-14T04:33:19.6610031Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0
2025-03-14T04:33:19.6612046Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file
2025-03-14T04:33:19.6614151Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0
2025-03-14T04:33:19.6616214Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file
2025-03-14T04:33:19.6618307Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0
2025-03-14T04:33:19.6620456Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file
2025-03-14T04:33:19.6622614Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0
2025-03-14T04:33:19.6624701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file
2025-03-14T04:33:19.6626824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0
2025-03-14T04:33:19.6628877Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file
2025-03-14T04:33:19.6630943Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0
2025-03-14T04:33:19.6632935Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file
2025-03-14T04:33:19.6634936Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0
2025-03-14T04:33:19.6637100Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 117 <- wrt source file
2025-03-14T04:33:19.6639474Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0
2025-03-14T04:33:19.6641718Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file
2025-03-14T04:33:19.6643980Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0
2025-03-14T04:33:19.6646239Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file
2025-03-14T04:33:19.6648591Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0
2025-03-14T04:33:19.6650999Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file
2025-03-14T04:33:19.6653242Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0
2025-03-14T04:33:19.6655430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file
2025-03-14T04:33:19.6657753Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0
2025-03-14T04:33:19.6660060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file
2025-03-14T04:33:19.6662379Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0
2025-03-14T04:33:19.6664602Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file
2025-03-14T04:33:19.6666853Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0
2025-03-14T04:33:19.6669070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file
2025-03-14T04:33:19.6671360Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0
2025-03-14T04:33:19.6673664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file
2025-03-14T04:33:19.6676114Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0
2025-03-14T04:33:19.6678371Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file
2025-03-14T04:33:19.6680628Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0
2025-03-14T04:33:19.6682815Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file
2025-03-14T04:33:19.6685038Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0
2025-03-14T04:33:19.6687227Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file
2025-03-14T04:33:19.6689458Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0
2025-03-14T04:33:19.6691701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file
2025-03-14T04:33:19.6694124Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0
2025-03-14T04:33:19.6696336Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file
2025-03-14T04:33:19.6698547Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0
2025-03-14T04:33:19.6700704Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file
2025-03-14T04:33:19.6702931Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0
2025-03-14T04:33:19.6705105Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file
2025-03-14T04:33:19.6707324Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0
2025-03-14T04:33:19.6709496Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file
2025-03-14T04:33:19.6711703Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0
2025-03-14T04:33:19.6713907Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file
2025-03-14T04:33:19.6716181Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0
2025-03-14T04:33:19.6718435Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file
2025-03-14T04:33:19.6720844Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0
2025-03-14T04:33:19.6723147Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file
2025-03-14T04:33:19.6725479Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0
2025-03-14T04:33:19.6727772Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file
2025-03-14T04:33:19.6730119Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0
2025-03-14T04:33:19.6732457Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file
2025-03-14T04:33:19.6734756Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0
2025-03-14T04:33:19.6737032Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file
2025-03-14T04:33:19.6739391Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0
2025-03-14T04:33:19.6741819Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1031 <- wrt source file
2025-03-14T04:33:19.6744383Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0
2025-03-14T04:33:19.6746734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1494 <- wrt source file
2025-03-14T04:33:19.6749187Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0
2025-03-14T04:33:19.6751445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1536 <- wrt source file
2025-03-14T04:33:19.6753797Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0
2025-03-14T04:33:19.6756099Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1559 <- wrt source file
2025-03-14T04:33:19.6758465Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0
2025-03-14T04:33:19.6760792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1594 <- wrt source file
2025-03-14T04:33:19.6763184Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0
2025-03-14T04:33:19.6765792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1652 <- wrt source file
2025-03-14T04:33:19.6768451Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0
2025-03-14T04:33:19.6771016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1693 <- wrt source file
2025-03-14T04:33:19.6773475Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0
2025-03-14T04:33:19.6775787Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1729 <- wrt source file
2025-03-14T04:33:19.6778166Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0
2025-03-14T04:33:19.6780534Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file
2025-03-14T04:33:19.6783278Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0
2025-03-14T04:33:19.6785954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file
2025-03-14T04:33:19.7075729Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0
2025-03-14T04:33:19.7078628Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file
2025-03-14T04:33:19.9564574Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0
2025-03-14T04:33:19.9729605Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file
2025-03-14T04:33:19.9749459Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0
2025-03-14T04:33:19.9751878Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 76 <- wrt source file
2025-03-14T04:33:19.9754237Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0
2025-03-14T04:33:19.9756543Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 282 <- wrt source file
2025-03-14T04:33:19.9758877Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0
2025-03-14T04:33:19.9761448Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 464 <- wrt source file
2025-03-14T04:33:19.9763788Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0
2025-03-14T04:33:19.9766123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 596 <- wrt source file
2025-03-14T04:33:19.9768538Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0
2025-03-14T04:33:19.9770891Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 748 <- wrt source file
2025-03-14T04:33:19.9773287Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0
2025-03-14T04:33:19.9775673Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file
2025-03-14T04:33:19.9778122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0
2025-03-14T04:33:19.9780509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file
2025-03-14T04:33:19.9783093Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0
2025-03-14T04:33:19.9785378Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file
2025-03-14T04:33:19.9787590Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0
2025-03-14T04:33:19.9789795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file
2025-03-14T04:33:19.9792066Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0
2025-03-14T04:33:19.9794280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file
2025-03-14T04:33:19.9804845Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0
2025-03-14T04:33:19.9807444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file
2025-03-14T04:33:19.9882140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0
2025-03-14T04:33:19.9884807Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file
2025-03-14T04:33:19.9887332Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0
2025-03-14T04:33:19.9889711Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file
2025-03-14T04:33:19.9963769Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0
2025-03-14T04:33:19.9966446Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file
2025-03-14T04:33:19.9970046Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0
2025-03-14T04:33:19.9972333Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file
2025-03-14T04:33:19.9975914Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0
2025-03-14T04:33:19.9977989Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file
2025-03-14T04:33:19.9990519Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0
2025-03-14T04:33:19.9992826Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file
2025-03-14T04:33:20.0003627Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0
2025-03-14T04:33:20.0006104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file
2025-03-14T04:33:20.0196123Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0
2025-03-14T04:33:20.0198970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file
2025-03-14T04:33:20.2681768Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0
2025-03-14T04:33:20.2855169Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file
2025-03-14T04:33:20.2857551Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0
2025-03-14T04:33:20.2859774Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file
2025-03-14T04:33:20.2864578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0
2025-03-14T04:33:20.2866721Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file
2025-03-14T04:33:20.2873563Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0
2025-03-14T04:33:20.2875737Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file
2025-03-14T04:33:20.2895288Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0
2025-03-14T04:33:20.2897424Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file
2025-03-14T04:33:20.2903642Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0
2025-03-14T04:33:20.2905749Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file
2025-03-14T04:33:20.2931981Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0
2025-03-14T04:33:20.2934576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file
2025-03-14T04:33:20.2939696Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0
2025-03-14T04:33:20.2941977Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file
2025-03-14T04:33:20.2955972Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0
2025-03-14T04:33:20.2958198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 519 <- wrt source file
2025-03-14T04:33:20.2965200Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0
2025-03-14T04:33:20.2967357Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 597 <- wrt source file
2025-03-14T04:33:20.2972137Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0
2025-03-14T04:33:20.2974255Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 679 <- wrt source file
2025-03-14T04:33:20.2978922Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0
2025-03-14T04:33:20.2981163Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 750 <- wrt source file
2025-03-14T04:33:20.2991167Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0
2025-03-14T04:33:20.2993686Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 943 <- wrt source file
2025-03-14T04:33:20.2999822Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0
2025-03-14T04:33:20.3002216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1265 <- wrt source file
2025-03-14T04:33:20.3009127Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0
2025-03-14T04:33:20.3011487Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1405 <- wrt source file
2025-03-14T04:33:20.3018704Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0
2025-03-14T04:33:20.3021082Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1470 <- wrt source file
2025-03-14T04:33:20.3026285Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0
2025-03-14T04:33:20.3028729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1549 <- wrt source file
2025-03-14T04:33:20.3035122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0
2025-03-14T04:33:20.3037466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1649 <- wrt source file
2025-03-14T04:33:20.3047944Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0
2025-03-14T04:33:20.3050410Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1890 <- wrt source file
2025-03-14T04:33:20.3074638Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0
2025-03-14T04:33:20.3077052Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 538 <- wrt source file
2025-03-14T04:33:20.3079867Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0
2025-03-14T04:33:20.3082296Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1020 <- wrt source file
2025-03-14T04:33:20.3089476Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0
2025-03-14T04:33:20.3091742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1274 <- wrt source file
2025-03-14T04:33:20.3096302Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0
2025-03-14T04:33:20.3098609Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2192 <- wrt source file
2025-03-14T04:33:20.3101065Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0
2025-03-14T04:33:20.3103450Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2634 <- wrt source file
2025-03-14T04:33:20.3105877Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0
2025-03-14T04:33:20.3108427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2662 <- wrt source file
2025-03-14T04:33:20.3111038Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0
2025-03-14T04:33:20.3113433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2689 <- wrt source file
2025-03-14T04:33:20.3115786Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0
2025-03-14T04:33:20.3118141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2716 <- wrt source file
2025-03-14T04:33:20.3120670Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0
2025-03-14T04:33:20.3123106Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2747 <- wrt source file
2025-03-14T04:33:20.3125584Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0
2025-03-14T04:33:20.3128030Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2771 <- wrt source file
2025-03-14T04:33:20.3130386Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0
2025-03-14T04:33:20.3132741Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2809 <- wrt source file
2025-03-14T04:33:20.3135238Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0
2025-03-14T04:33:20.3137753Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file
2025-03-14T04:33:20.3147277Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0
2025-03-14T04:33:20.3150745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file
2025-03-14T04:33:20.3157245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0
2025-03-14T04:33:20.3160088Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file
2025-03-14T04:33:20.3165605Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0
2025-03-14T04:33:20.3168331Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file
2025-03-14T04:33:20.3171157Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0
2025-03-14T04:33:20.3173481Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file
2025-03-14T04:33:20.3177749Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0
2025-03-14T04:33:20.3180068Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file
2025-03-14T04:33:20.3197626Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0
2025-03-14T04:33:20.3200124Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file
2025-03-14T04:33:20.9221468Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0
2025-03-14T04:33:20.9541734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file
2025-03-14T04:33:20.9550972Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0
2025-03-14T04:33:20.9553294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file
2025-03-14T04:33:20.9557575Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0
2025-03-14T04:33:20.9559951Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file
2025-03-14T04:33:20.9580923Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0
2025-03-14T04:33:20.9583581Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file
2025-03-14T04:33:20.9587578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0
2025-03-14T04:33:20.9589939Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file
2025-03-14T04:33:20.9593785Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0
2025-03-14T04:33:20.9596167Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file
2025-03-14T04:33:20.9598590Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0
2025-03-14T04:33:20.9601014Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file
2025-03-14T04:33:20.9603931Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0
2025-03-14T04:33:20.9606332Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file
2025-03-14T04:33:20.9610093Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0
2025-03-14T04:33:20.9612492Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file
2025-03-14T04:33:21.4562376Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0
2025-03-14T04:33:21.4881789Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file
2025-03-14T04:33:21.4891026Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0
2025-03-14T04:33:21.4893267Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file
2025-03-14T04:33:21.4897349Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0
2025-03-14T04:33:21.4899847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file
2025-03-14T04:33:21.4922243Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0
2025-03-14T04:33:21.4924589Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file
2025-03-14T04:33:21.4927620Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0
2025-03-14T04:33:21.4930067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file
2025-03-14T04:33:21.4932978Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0
2025-03-14T04:33:21.4935307Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file
2025-03-14T04:33:21.4939359Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0
2025-03-14T04:33:21.4941748Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file
2025-03-14T04:33:21.4984794Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0
2025-03-14T04:33:21.4987038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file
2025-03-14T04:33:21.6947233Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0
2025-03-14T04:33:21.7006907Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file
2025-03-14T04:33:21.7017420Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0
2025-03-14T04:33:21.7019737Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file
2025-03-14T04:33:21.7750917Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0
2025-03-14T04:33:21.7753232Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file
2025-03-14T04:33:21.7759943Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0
2025-03-14T04:33:21.7762183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file
2025-03-14T04:33:21.7796551Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0
2025-03-14T04:33:21.7798806Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file
2025-03-14T04:33:21.9437825Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0
2025-03-14T04:33:21.9496949Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file
2025-03-14T04:33:21.9541488Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0
2025-03-14T04:33:21.9543976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file
2025-03-14T04:33:22.0133730Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0
2025-03-14T04:33:22.0136137Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file
2025-03-14T04:33:22.0141633Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0
2025-03-14T04:33:22.0144235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file
2025-03-14T04:33:22.0189314Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0
2025-03-14T04:33:22.0192364Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file
2025-03-14T04:33:22.2153302Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0
2025-03-14T04:33:22.2212482Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file
2025-03-14T04:33:22.2220205Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0
2025-03-14T04:33:22.2223116Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file
2025-03-14T04:33:22.2230929Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0
2025-03-14T04:33:22.2233814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file
2025-03-14T04:33:22.2256542Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0
2025-03-14T04:33:22.2324108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file
2025-03-14T04:33:22.2329650Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0
2025-03-14T04:33:22.2332942Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file
2025-03-14T04:33:22.2340112Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0
2025-03-14T04:33:22.2343032Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file
2025-03-14T04:33:22.2362456Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0
2025-03-14T04:33:22.2364996Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 591 <- wrt source file
2025-03-14T04:33:22.2376113Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0
2025-03-14T04:33:22.2379005Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 948 <- wrt source file
2025-03-14T04:33:22.2726726Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0
2025-03-14T04:33:22.2729424Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file
2025-03-14T04:33:22.2745941Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0
2025-03-14T04:33:22.2749106Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1536 <- wrt source file
2025-03-14T04:33:22.2758483Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0
2025-03-14T04:33:22.2761094Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1658 <- wrt source file
2025-03-14T04:33:22.2771015Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0
2025-03-14T04:33:22.2774006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1772 <- wrt source file
2025-03-14T04:33:22.2786407Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0
2025-03-14T04:33:22.2789353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file
2025-03-14T04:33:22.2803750Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0
2025-03-14T04:33:22.2806168Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file
2025-03-14T04:33:22.2809583Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0
2025-03-14T04:33:22.2812139Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file
2025-03-14T04:33:22.2816245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0
2025-03-14T04:33:22.2818724Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 88 <- wrt source file
2025-03-14T04:33:23.0473521Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0
2025-03-14T04:33:23.0491072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 256 <- wrt source file
2025-03-14T04:33:23.0493574Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0
2025-03-14T04:33:23.0495890Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 326 <- wrt source file
2025-03-14T04:33:23.1481947Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0
2025-03-14T04:33:23.1529967Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 544 <- wrt source file
2025-03-14T04:33:23.3632954Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0
2025-03-14T04:33:23.3640818Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 667 <- wrt source file
2025-03-14T04:33:23.3922623Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0
2025-03-14T04:33:23.3925580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 973 <- wrt source file
2025-03-14T04:33:23.4431759Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0
2025-03-14T04:33:23.4434485Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file
2025-03-14T04:33:23.4455251Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0
2025-03-14T04:33:23.4457681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file
2025-03-14T04:33:23.4466698Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0
2025-03-14T04:33:23.4469252Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file
2025-03-14T04:33:23.4474361Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0
2025-03-14T04:33:23.4476959Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 127 <- wrt source file
2025-03-14T04:33:23.4479489Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0
2025-03-14T04:33:23.4482074Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 625 <- wrt source file
2025-03-14T04:33:23.4484802Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0
2025-03-14T04:33:23.4487552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1423 <- wrt source file
2025-03-14T04:33:23.4490458Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0
2025-03-14T04:33:23.4493412Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1975 <- wrt source file
2025-03-14T04:33:23.4496571Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0
2025-03-14T04:33:23.4499612Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1985 <- wrt source file
2025-03-14T04:33:23.4502699Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1
2025-03-14T04:33:23.4505824Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2020 <- wrt source file
2025-03-14T04:33:23.4509052Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0
2025-03-14T04:33:23.4511877Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file
2025-03-14T04:33:23.4514488Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0
2025-03-14T04:33:23.4516807Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file
2025-03-14T04:33:23.4519076Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0
2025-03-14T04:33:23.4521422Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file
2025-03-14T04:33:23.4523885Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0
2025-03-14T04:33:23.4526322Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file
2025-03-14T04:33:23.4528801Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0
2025-03-14T04:33:23.4531255Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file
2025-03-14T04:33:23.4533789Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0
2025-03-14T04:33:23.4536386Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 505 <- wrt source file
2025-03-14T04:33:23.4539017Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0
2025-03-14T04:33:23.4541353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 844 <- wrt source file
2025-03-14T04:33:23.4543493Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0
2025-03-14T04:33:23.4545711Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 880 <- wrt source file
2025-03-14T04:33:23.4548073Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0
2025-03-14T04:33:23.4550534Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 923 <- wrt source file
2025-03-14T04:33:23.4552853Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0
2025-03-14T04:33:23.4555103Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1190 <- wrt source file
2025-03-14T04:33:23.4557239Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0
2025-03-14T04:33:23.4559385Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1218 <- wrt source file
2025-03-14T04:33:23.4561550Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0
2025-03-14T04:33:23.4563750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 354 <- wrt source file
2025-03-14T04:33:23.4566059Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0
2025-03-14T04:33:23.4568257Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 432 <- wrt source file
2025-03-14T04:33:23.4570424Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0
2025-03-14T04:33:23.4572573Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 490 <- wrt source file
2025-03-14T04:33:23.4574874Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0
2025-03-14T04:33:23.4577152Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 546 <- wrt source file
2025-03-14T04:33:23.4579348Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0
2025-03-14T04:33:23.4581534Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 574 <- wrt source file
2025-03-14T04:33:23.4583771Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0
2025-03-14T04:33:23.4586052Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file
2025-03-14T04:33:23.4589013Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0
2025-03-14T04:33:23.4591451Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file
2025-03-14T04:33:23.4596286Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0
2025-03-14T04:33:23.4598727Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 196 <- wrt source file
2025-03-14T04:33:23.4601169Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0
2025-03-14T04:33:23.4603459Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file
2025-03-14T04:33:23.4606896Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0
2025-03-14T04:33:23.4609245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file
2025-03-14T04:33:23.4611940Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0
2025-03-14T04:33:23.4614481Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 315 <- wrt source file
2025-03-14T04:33:23.4617083Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0
2025-03-14T04:33:23.4619937Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file
2025-03-14T04:33:23.4636723Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0
2025-03-14T04:33:23.4639438Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 258 <- wrt source file
2025-03-14T04:33:23.4641690Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0
2025-03-14T04:33:23.4643974Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 353 <- wrt source file
2025-03-14T04:33:23.4646378Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0
2025-03-14T04:33:23.4648624Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 446 <- wrt source file
2025-03-14T04:33:23.4651098Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0
2025-03-14T04:33:23.4653312Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 499 <- wrt source file
2025-03-14T04:33:23.4655612Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0
2025-03-14T04:33:23.4657839Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 557 <- wrt source file
2025-03-14T04:33:23.4660093Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0
2025-03-14T04:33:23.4662274Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 628 <- wrt source file
2025-03-14T04:33:23.4664500Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0
2025-03-14T04:33:23.4666790Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 748 <- wrt source file
2025-03-14T04:33:23.4669093Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0
2025-03-14T04:33:23.4671357Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 889 <- wrt source file
2025-03-14T04:33:23.4673657Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0
2025-03-14T04:33:23.4675970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1037 <- wrt source file
2025-03-14T04:33:23.4678384Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0
2025-03-14T04:33:23.4680809Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1174 <- wrt source file
2025-03-14T04:33:23.4683276Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0
2025-03-14T04:33:23.4685643Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1414 <- wrt source file
2025-03-14T04:33:23.4687881Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0
2025-03-14T04:33:23.4690363Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1676 <- wrt source file
2025-03-14T04:33:23.4693124Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0
2025-03-14T04:33:23.4695849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1692 <- wrt source file
2025-03-14T04:33:23.4698607Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1
2025-03-14T04:33:23.4701060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1830 <- wrt source file
2025-03-14T04:33:23.4703329Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0
2025-03-14T04:33:23.4705490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 331 <- wrt source file
2025-03-14T04:33:23.4707757Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0
2025-03-14T04:33:23.4709921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file
2025-03-14T04:33:23.4712154Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0
2025-03-14T04:33:23.4714682Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 283 <- wrt source file
2025-03-14T04:33:23.4717539Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0
2025-03-14T04:33:23.4720070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 605 <- wrt source file
2025-03-14T04:33:23.4722275Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0
2025-03-14T04:33:23.4724693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 338 <- wrt source file
2025-03-14T04:33:23.4727322Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0
2025-03-14T04:33:23.4739942Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file
2025-03-14T04:33:23.4742257Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0
2025-03-14T04:33:23.4744641Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 614 <- wrt source file
2025-03-14T04:33:23.4747187Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0
2025-03-14T04:33:23.4749872Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 735 <- wrt source file
2025-03-14T04:33:23.4752606Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0
2025-03-14T04:33:23.4755099Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 824 <- wrt source file
2025-03-14T04:33:23.4757619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0
2025-03-14T04:33:23.4760335Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4649 <- wrt source file
2025-03-14T04:33:23.4763160Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0
2025-03-14T04:33:23.4765939Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4663 <- wrt source file
2025-03-14T04:33:23.4768757Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0
2025-03-14T04:33:23.4771505Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4693 <- wrt source file
2025-03-14T04:33:23.4774358Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0
2025-03-14T04:33:23.4777030Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 194 <- wrt source file
2025-03-14T04:33:23.4779611Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0
2025-03-14T04:33:23.4782226Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0, line 220 <- wrt source file
2025-03-14T04:33:23.4784972Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0
2025-03-14T04:33:23.4787857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 313 <- wrt source file
2025-03-14T04:33:23.4791005Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0
2025-03-14T04:33:23.4794186Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file
2025-03-14T04:33:23.4797378Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0
2025-03-14T04:33:23.4800050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 247 <- wrt source file
2025-03-14T04:33:23.4802299Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0
2025-03-14T04:33:23.4804514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 290 <- wrt source file
2025-03-14T04:33:23.4806809Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0
2025-03-14T04:33:23.4809043Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 327 <- wrt source file
2025-03-14T04:33:23.4811395Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0
2025-03-14T04:33:23.4813599Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 357 <- wrt source file
2025-03-14T04:33:23.4815785Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0
2025-03-14T04:33:23.4817965Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 392 <- wrt source file
2025-03-14T04:33:23.4820187Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0
2025-03-14T04:33:23.4822412Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 427 <- wrt source file
2025-03-14T04:33:23.4824717Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0
2025-03-14T04:33:23.4826898Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 464 <- wrt source file
2025-03-14T04:33:23.4829081Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0
2025-03-14T04:33:23.4831346Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 880 <- wrt source file
2025-03-14T04:33:23.4833735Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0
2025-03-14T04:33:23.4836034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 268 <- wrt source file
2025-03-14T04:33:23.4838357Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0
2025-03-14T04:33:23.4840664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0, line 328 <- wrt source file
2025-03-14T04:33:23.4842948Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0
2025-03-14T04:33:23.4845097Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 1115 <- wrt source file
2025-03-14T04:33:23.4847200Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0
2025-03-14T04:33:23.4849798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file
2025-03-14T04:33:23.4852563Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0
2025-03-14T04:33:23.4855407Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file
2025-03-14T04:33:23.4858408Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0
2025-03-14T04:33:23.4861147Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file
2025-03-14T04:33:23.4863775Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0
2025-03-14T04:33:23.4866379Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 547 <- wrt source file
2025-03-14T04:33:23.4868872Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0
2025-03-14T04:33:23.4871335Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 749 <- wrt source file
2025-03-14T04:33:23.4873864Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0
2025-03-14T04:33:23.4876164Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file
2025-03-14T04:33:23.4878327Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0
2025-03-14T04:33:23.4880791Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 718 <- wrt source file
2025-03-14T04:33:23.5201268Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0
2025-03-14T04:33:23.5203798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file
2025-03-14T04:33:23.5206370Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0
2025-03-14T04:33:23.5208756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 219 <- wrt source file
2025-03-14T04:33:23.5211098Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0
2025-03-14T04:33:23.5213375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::random_split:0, line 441 <- wrt source file
2025-03-14T04:33:23.5215688Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::random_split:0
2025-03-14T04:33:23.5217904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::Sampler:0, line 34 <- wrt source file
2025-03-14T04:33:23.5220121Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::Sampler:0
2025-03-14T04:33:23.5222480Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 232 <- wrt source file
2025-03-14T04:33:23.5225139Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0
2025-03-14T04:33:23.5227527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 295 <- wrt source file
2025-03-14T04:33:23.5229860Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0
2025-03-14T04:33:23.5232210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file
2025-03-14T04:33:23.5234709Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0
2025-03-14T04:33:23.5237079Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file
2025-03-14T04:33:23.5239467Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0
2025-03-14T04:33:23.5241916Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file
2025-03-14T04:33:23.5244448Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0
2025-03-14T04:33:23.5246978Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 97 <- wrt source file
2025-03-14T04:33:23.5249746Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0
2025-03-14T04:33:23.5252298Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 264 <- wrt source file
2025-03-14T04:33:23.5254903Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0
2025-03-14T04:33:23.5257587Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 52 <- wrt source file
2025-03-14T04:33:23.5260452Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0
2025-03-14T04:33:23.5263286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 198 <- wrt source file
2025-03-14T04:33:23.5266350Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0
2025-03-14T04:33:23.5269285Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 88 <- wrt source file
2025-03-14T04:33:23.5272320Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0
2025-03-14T04:33:23.5275238Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 38 <- wrt source file
2025-03-14T04:33:23.5278181Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0
2025-03-14T04:33:23.5281083Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 88 <- wrt source file
2025-03-14T04:33:23.5283978Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0
2025-03-14T04:33:23.5286804Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 307 <- wrt source file
2025-03-14T04:33:23.5289583Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0
2025-03-14T04:33:23.5292445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 393 <- wrt source file
2025-03-14T04:33:23.5295502Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0
2025-03-14T04:33:23.5298469Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 603 <- wrt source file
2025-03-14T04:33:23.5301487Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0
2025-03-14T04:33:23.5304411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 671 <- wrt source file
2025-03-14T04:33:23.5307305Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0
2025-03-14T04:33:23.5310189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 31 <- wrt source file
2025-03-14T04:33:23.5313195Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0
2025-03-14T04:33:23.5316143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 35 <- wrt source file
2025-03-14T04:33:23.5319180Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0
2025-03-14T04:33:23.5322049Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 53 <- wrt source file
2025-03-14T04:33:23.5324933Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0
2025-03-14T04:33:23.5327831Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 113 <- wrt source file
2025-03-14T04:33:23.5330799Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0
2025-03-14T04:33:23.5333664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 180 <- wrt source file
2025-03-14T04:33:23.5336566Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0
2025-03-14T04:33:23.5339406Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 37 <- wrt source file
2025-03-14T04:33:23.5342281Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0
2025-03-14T04:33:23.5345229Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 25 <- wrt source file
2025-03-14T04:33:23.5348365Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0
2025-03-14T04:33:23.5351519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file
2025-03-14T04:33:23.5354503Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0
2025-03-14T04:33:23.5357353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file
2025-03-14T04:33:23.5360265Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0
2025-03-14T04:33:23.5363136Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 34 <- wrt source file
2025-03-14T04:33:23.5366229Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0
2025-03-14T04:33:23.5369123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 29 <- wrt source file
2025-03-14T04:33:23.5372015Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0
2025-03-14T04:33:23.5374826Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 73 <- wrt source file
2025-03-14T04:33:23.5377697Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0
2025-03-14T04:33:23.5380494Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 29 <- wrt source file
2025-03-14T04:33:23.5383332Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0
2025-03-14T04:33:23.5386173Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file
2025-03-14T04:33:23.5389173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0
2025-03-14T04:33:23.5392022Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 37 <- wrt source file
2025-03-14T04:33:23.5394815Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0
2025-03-14T04:33:23.5397533Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file
2025-03-14T04:33:23.5400304Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0
2025-03-14T04:33:23.5404165Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 440 <- wrt source file
2025-03-14T04:33:23.5406771Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0
2025-03-14T04:33:23.5409413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 536 <- wrt source file
2025-03-14T04:33:23.5412075Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0
2025-03-14T04:33:23.5414692Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file
2025-03-14T04:33:23.5417341Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0
2025-03-14T04:33:23.5420001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file
2025-03-14T04:33:23.5422742Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0
2025-03-14T04:33:23.5425421Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file
2025-03-14T04:33:23.5428173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0
2025-03-14T04:33:23.5430846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file
2025-03-14T04:33:23.5433575Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0
2025-03-14T04:33:23.5436234Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file
2025-03-14T04:33:23.5438972Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0
2025-03-14T04:33:23.5441710Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file
2025-03-14T04:33:23.5444495Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0
2025-03-14T04:33:23.5447269Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file
2025-03-14T04:33:23.5450343Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0
2025-03-14T04:33:23.5453124Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file
2025-03-14T04:33:23.5455814Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0
2025-03-14T04:33:23.5458465Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file
2025-03-14T04:33:23.5461181Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0
2025-03-14T04:33:23.5463818Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file
2025-03-14T04:33:23.5466488Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0
2025-03-14T04:33:23.5469220Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file
2025-03-14T04:33:23.5471994Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0
2025-03-14T04:33:23.5474690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file
2025-03-14T04:33:23.5477429Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0
2025-03-14T04:33:23.5480398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file
2025-03-14T04:33:23.5483561Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0
2025-03-14T04:33:23.5486677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file
2025-03-14T04:33:23.5489762Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0
2025-03-14T04:33:23.5492696Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file
2025-03-14T04:33:23.5495574Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0
2025-03-14T04:33:23.5498310Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file
2025-03-14T04:33:23.5500990Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0
2025-03-14T04:33:23.5502383Z ============
2025-03-14T04:33:23.5502880Z Finished doctests
2025-03-14T04:33:23.5503309Z 342 / 709 passed
2025-03-14T04:33:23.5503744Z 
2025-03-14T04:33:23.5504283Z === Found 116 parse-time warnings ===
2025-03-14T04:33:23.5505049Z --- Parse Warning: 1 / 116 ---
2025-03-14T04:33:23.5507053Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1507.
2025-03-14T04:33:23.5509442Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5510303Z     
2025-03-14T04:33:23.5510740Z     dim_order(ambiguity_check=False) -> tuple
2025-03-14T04:33:23.5511326Z     
2025-03-14T04:33:23.5511962Z     Returns the uniquely determined tuple of int describing the dim order or
2025-03-14T04:33:23.5512817Z     physical layout of :attr:`self`.
2025-03-14T04:33:23.5513350Z     
2025-03-14T04:33:23.5514013Z     The dim order represents how dimensions are laid out in memory of dense tensors,
2025-03-14T04:33:23.5515023Z     starting from the outermost to the innermost dimension.
2025-03-14T04:33:23.5515693Z     
2025-03-14T04:33:23.5516244Z     Note that the dim order may not always be uniquely determined.
2025-03-14T04:33:23.5517518Z     If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined;
2025-03-14T04:33:23.5519240Z     If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted
2025-03-14T04:33:23.5520666Z     into exactly one of the given memory formats, or it cannot be uniquely determined.
2025-03-14T04:33:23.5521996Z     If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness.
2025-03-14T04:33:23.5523023Z     Otherwise, it will raise TypeError.
2025-03-14T04:33:23.5523580Z     
2025-03-14T04:33:23.5523941Z     Args:
2025-03-14T04:33:23.5524758Z         ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order.
2025-03-14T04:33:23.5525727Z     
2025-03-14T04:33:23.5526121Z     Examples::
2025-03-14T04:33:23.5526525Z     
2025-03-14T04:33:23.5526939Z         >>> torch.empty((2, 3, 5, 7)).dim_order()
2025-03-14T04:33:23.5527510Z         (0, 1, 2, 3)
2025-03-14T04:33:23.5528053Z         >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order()
2025-03-14T04:33:23.5528705Z         (0, 2, 1, 3)
2025-03-14T04:33:23.5529346Z         >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order()
2025-03-14T04:33:23.5530086Z         (0, 2, 3, 1)
2025-03-14T04:33:23.5530552Z         >>> torch.empty((1, 2, 3, 4)).dim_order()
2025-03-14T04:33:23.5531118Z         (0, 1, 2, 3)
2025-03-14T04:33:23.5531538Z         >>> try:
2025-03-14T04:33:23.5532098Z         ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True)
2025-03-14T04:33:23.5532830Z         ... except RuntimeError as e:
2025-03-14T04:33:23.5533425Z         ...     print(e)
2025-03-14T04:33:23.5534284Z         The tensor does not have unique dim order, or cannot map to exact one of the given memory formats.
2025-03-14T04:33:23.5535285Z         >>> torch.empty((1, 2, 3, 4)).dim_order(
2025-03-14T04:33:23.5536076Z         ...     ambiguity_check=[torch.contiguous_format, torch.channels_last]
2025-03-14T04:33:23.5536918Z         ... )  # It can be mapped to contiguous format
2025-03-14T04:33:23.5537520Z         (0, 1, 2, 3)
2025-03-14T04:33:23.5537944Z         >>> try:
2025-03-14T04:33:23.5538528Z         ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL")
2025-03-14T04:33:23.5539282Z         ... except TypeError as e:
2025-03-14T04:33:23.5539816Z         ...     print(e)
2025-03-14T04:33:23.5540532Z         The ambiguity_check argument must be a bool or a list of memory formats.
2025-03-14T04:33:23.5541327Z     
2025-03-14T04:33:23.5541679Z     .. warning::
2025-03-14T04:33:23.5542296Z         The dim_order tensor API is experimental and subject to change.
2025-03-14T04:33:23.5543028Z     
2025-03-14T04:33:23.5543696Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.5544530Z     
2025-03-14T04:33:23.5544914Z       warnings.warn(msg)
2025-03-14T04:33:23.5545383Z     
2025-03-14T04:33:23.5545959Z --- Parse Warning: 2 / 116 ---
2025-03-14T04:33:23.5547982Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=446.
2025-03-14T04:33:23.5550407Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5551554Z     Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2025-03-14T04:33:23.5552365Z     
2025-03-14T04:33:23.5552887Z             This is helpful when you want to visualize data over some
2025-03-14T04:33:23.5553715Z             range of inputs. See below for a plotting example.
2025-03-14T04:33:23.5554350Z     
2025-03-14T04:33:23.5554851Z             Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2025-03-14T04:33:23.5555707Z             inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2025-03-14T04:33:23.5556622Z             this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2025-03-14T04:33:23.5557481Z             G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2025-03-14T04:33:23.5558320Z             the output :math:`G_i` is constructed by expanding :math:`T_i`
2025-03-14T04:33:23.5559072Z             to the result shape.
2025-03-14T04:33:23.5559614Z     
2025-03-14T04:33:23.5560055Z             .. note::
2025-03-14T04:33:23.5560619Z                 0D inputs are treated equivalently to 1D inputs of a
2025-03-14T04:33:23.5561302Z                 single element.
2025-03-14T04:33:23.5561783Z     
2025-03-14T04:33:23.5562160Z             .. warning::
2025-03-14T04:33:23.5562791Z                 `torch.meshgrid(*tensors)` currently has the same behavior
2025-03-14T04:33:23.5563635Z                 as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2025-03-14T04:33:23.5564297Z     
2025-03-14T04:33:23.5564764Z                 In the future `torch.meshgrid` will transition to
2025-03-14T04:33:23.5565464Z                 `indexing='xy'` as the default.
2025-03-14T04:33:23.5566031Z     
2025-03-14T04:33:23.5566569Z                 https://github.com/pytorch/pytorch/issues/50276 tracks
2025-03-14T04:33:23.5567444Z                 this issue with the goal of migrating to NumPy's behavior.
2025-03-14T04:33:23.5568148Z     
2025-03-14T04:33:23.5568519Z             .. seealso::
2025-03-14T04:33:23.5568967Z     
2025-03-14T04:33:23.5569472Z                 :func:`torch.cartesian_prod` has the same effect but it
2025-03-14T04:33:23.5570254Z                 collects the data in a tensor of vectors.
2025-03-14T04:33:23.5570851Z     
2025-03-14T04:33:23.5571219Z             Args:
2025-03-14T04:33:23.5572007Z                 tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2025-03-14T04:33:23.5573012Z                     treated as tensors of size :math:`(1,)` automatically
2025-03-14T04:33:23.5573681Z     
2025-03-14T04:33:23.5574223Z                 indexing: (str, optional): the indexing mode, either "xy"
2025-03-14T04:33:23.5575079Z                     or "ij", defaults to "ij". See warning for future changes.
2025-03-14T04:33:23.5575761Z     
2025-03-14T04:33:23.5576239Z                     If "xy" is selected, the first dimension corresponds
2025-03-14T04:33:23.5577041Z                     to the cardinality of the second input and the second
2025-03-14T04:33:23.5577879Z                     dimension corresponds to the cardinality of the first
2025-03-14T04:33:23.5578584Z                     input.
2025-03-14T04:33:23.5579048Z     
2025-03-14T04:33:23.5579527Z                     If "ij" is selected, the dimensions are in the same
2025-03-14T04:33:23.5580264Z                     order as the cardinality of the inputs.
2025-03-14T04:33:23.5580883Z     
2025-03-14T04:33:23.5581252Z             Returns:
2025-03-14T04:33:23.5581806Z                 seq (sequence of Tensors): If the input has :math:`N`
2025-03-14T04:33:23.5582605Z                 tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2025-03-14T04:33:23.5583439Z                 output will also have :math:`N` tensors, where each tensor
2025-03-14T04:33:23.5584220Z                 is of shape :math:`(S_0, ..., S_{N-1})`.
2025-03-14T04:33:23.5584925Z     
2025-03-14T04:33:23.5585302Z             Example::
2025-03-14T04:33:23.5585732Z     
2025-03-14T04:33:23.5586135Z                 >>> x = torch.tensor([1, 2, 3])
2025-03-14T04:33:23.5586748Z                 >>> y = torch.tensor([4, 5, 6])
2025-03-14T04:33:23.5587302Z     
2025-03-14T04:33:23.5587853Z                 Observe the element-wise pairings across the grid, (1, 4),
2025-03-14T04:33:23.5588651Z                 (1, 5), ..., (3, 6). This is the same thing as the
2025-03-14T04:33:23.5589294Z                 cartesian product.
2025-03-14T04:33:23.5589950Z                 >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2025-03-14T04:33:23.5590624Z                 >>> grid_x
2025-03-14T04:33:23.5591105Z                 tensor([[1, 1, 1],
2025-03-14T04:33:23.5591617Z                         [2, 2, 2],
2025-03-14T04:33:23.5592126Z                         [3, 3, 3]])
2025-03-14T04:33:23.5592619Z                 >>> grid_y
2025-03-14T04:33:23.5593092Z                 tensor([[4, 5, 6],
2025-03-14T04:33:23.5593609Z                         [4, 5, 6],
2025-03-14T04:33:23.5594111Z                         [4, 5, 6]])
2025-03-14T04:33:23.5594671Z     
2025-03-14T04:33:23.5595180Z                 This correspondence can be seen when these grids are
2025-03-14T04:33:23.5595888Z                 stacked properly.
2025-03-14T04:33:23.5596608Z                 >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2025-03-14T04:33:23.5597443Z                 ...             torch.cartesian_prod(x, y))
2025-03-14T04:33:23.5598051Z                 True
2025-03-14T04:33:23.5598481Z     
2025-03-14T04:33:23.5599006Z                 `torch.meshgrid` is commonly used to produce a grid for
2025-03-14T04:33:23.5599790Z                 plotting.
2025-03-14T04:33:23.5600330Z                 >>> # xdoctest: +REQUIRES(module:matplotlib)
2025-03-14T04:33:23.5601022Z                 >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2025-03-14T04:33:23.5601700Z                 >>> import matplotlib.pyplot as plt
2025-03-14T04:33:23.5602371Z                 >>> xs = torch.linspace(-5, 5, steps=100)
2025-03-14T04:33:23.5603037Z                 >>> ys = torch.linspace(-5, 5, steps=100)
2025-03-14T04:33:23.5603713Z                 >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2025-03-14T04:33:23.5604406Z                 >>> z = torch.sin(torch.sqrt(x * x + y * y))
2025-03-14T04:33:23.5605071Z                 >>> ax = plt.axes(projection='3d')
2025-03-14T04:33:23.5605819Z                 >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2025-03-14T04:33:23.5606489Z                 >>> plt.show()
2025-03-14T04:33:23.5606981Z     
2025-03-14T04:33:23.5607415Z             .. image:: ../_static/img/meshgrid.png
2025-03-14T04:33:23.5608015Z                 :width: 512
2025-03-14T04:33:23.5608479Z     
2025-03-14T04:33:23.5608831Z             
2025-03-14T04:33:23.5609536Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.5610367Z     
2025-03-14T04:33:23.5610758Z       warnings.warn(msg)
2025-03-14T04:33:23.5611210Z     
2025-03-14T04:33:23.5611790Z --- Parse Warning: 3 / 116 ---
2025-03-14T04:33:23.5613795Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=842.
2025-03-14T04:33:23.5616049Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5617450Z     unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
2025-03-14T04:33:23.5618521Z     
2025-03-14T04:33:23.5618993Z         Returns the unique elements of the input tensor.
2025-03-14T04:33:23.5619615Z     
2025-03-14T04:33:23.5620350Z         .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2025-03-14T04:33:23.5621477Z             this function also eliminates non-consecutive duplicate values.
2025-03-14T04:33:23.5622316Z     
2025-03-14T04:33:23.5622934Z         .. note:: Currently in the CUDA implementation and the CPU implementation,
2025-03-14T04:33:23.5624107Z             `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2025-03-14T04:33:23.5625368Z             Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2025-03-14T04:33:23.5626437Z             :func:`torch.unique_consecutive` which avoids the sorting.
2025-03-14T04:33:23.5627127Z     
2025-03-14T04:33:23.5627492Z         Args:
2025-03-14T04:33:23.5627941Z             input (Tensor): the input tensor
2025-03-14T04:33:23.5628740Z             sorted (bool): Whether to sort the unique elements in ascending order
2025-03-14T04:33:23.5629554Z                 before returning as output.
2025-03-14T04:33:23.5630337Z             return_inverse (bool): Whether to also return the indices for where
2025-03-14T04:33:23.5631350Z                 elements in the original input ended up in the returned unique list.
2025-03-14T04:33:23.5632390Z             return_counts (bool): Whether to also return the counts for each unique
2025-03-14T04:33:23.5633228Z                 element.
2025-03-14T04:33:23.5633899Z             dim (int, optional): the dimension to operate upon. If ``None``, the
2025-03-14T04:33:23.5634866Z                 unique of the flattened input is returned. Otherwise, each of the
2025-03-14T04:33:23.5635848Z                 tensors indexed by the given dimension is treated as one of the
2025-03-14T04:33:23.5636831Z                 elements to apply the unique operation upon. See examples for more
2025-03-14T04:33:23.5637655Z                 details. Default: ``None``
2025-03-14T04:33:23.5638211Z     
2025-03-14T04:33:23.5638558Z         Returns:
2025-03-14T04:33:23.5639403Z             (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2025-03-14T04:33:23.5640309Z     
2025-03-14T04:33:23.5640876Z                 - **output** (*Tensor*): the output list of unique scalar elements.
2025-03-14T04:33:23.5641709Z                 - **inverse_indices** (*Tensor*): (optional) if
2025-03-14T04:33:23.5642515Z                   :attr:`return_inverse` is True, there will be an additional
2025-03-14T04:33:23.5643445Z                   returned tensor (same shape as input) representing the indices
2025-03-14T04:33:23.5644396Z                   for where elements in the original input map to in the output;
2025-03-14T04:33:23.5645388Z                   otherwise, this function will only return a single tensor.
2025-03-14T04:33:23.5646178Z                 - **counts** (*Tensor*): (optional) if
2025-03-14T04:33:23.5646933Z                   :attr:`return_counts` is True, there will be an additional
2025-03-14T04:33:23.5647821Z                   returned tensor (same shape as output or output.size(dim),
2025-03-14T04:33:23.5648846Z                   if dim was specified) representing the number of occurrences
2025-03-14T04:33:23.5649628Z                   for each unique value or tensor.
2025-03-14T04:33:23.5650202Z     
2025-03-14T04:33:23.5650584Z         Example::
2025-03-14T04:33:23.5650998Z     
2025-03-14T04:33:23.5651591Z             >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2025-03-14T04:33:23.5652366Z             >>> output
2025-03-14T04:33:23.5652812Z             tensor([1, 2, 3])
2025-03-14T04:33:23.5653287Z     
2025-03-14T04:33:23.5653709Z             >>> output, inverse_indices = torch.unique(
2025-03-14T04:33:23.5654569Z             ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-14T04:33:23.5655383Z             >>> output
2025-03-14T04:33:23.5655833Z             tensor([1, 2, 3])
2025-03-14T04:33:23.5656330Z             >>> inverse_indices
2025-03-14T04:33:23.5656834Z             tensor([0, 2, 1, 2])
2025-03-14T04:33:23.5657322Z     
2025-03-14T04:33:23.5657765Z             >>> output, inverse_indices = torch.unique(
2025-03-14T04:33:23.5658763Z             ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-14T04:33:23.5659578Z             >>> output
2025-03-14T04:33:23.5660035Z             tensor([1, 2, 3])
2025-03-14T04:33:23.5660533Z             >>> inverse_indices
2025-03-14T04:33:23.5661024Z             tensor([[0, 2],
2025-03-14T04:33:23.5661504Z                     [1, 2]])
2025-03-14T04:33:23.5661967Z     
2025-03-14T04:33:23.5662359Z             >>> a = torch.tensor([
2025-03-14T04:33:23.5662872Z             ...     [
2025-03-14T04:33:23.5663306Z             ...         [1, 1, 0, 0],
2025-03-14T04:33:23.5663829Z             ...         [1, 1, 0, 0],
2025-03-14T04:33:23.5664358Z             ...         [0, 0, 1, 1],
2025-03-14T04:33:23.5664878Z             ...     ],
2025-03-14T04:33:23.5665313Z             ...     [
2025-03-14T04:33:23.5665764Z             ...         [0, 0, 1, 1],
2025-03-14T04:33:23.5666296Z             ...         [0, 0, 1, 1],
2025-03-14T04:33:23.5666817Z             ...         [1, 1, 1, 1],
2025-03-14T04:33:23.5667336Z             ...     ],
2025-03-14T04:33:23.5667761Z             ...     [
2025-03-14T04:33:23.5668200Z             ...         [1, 1, 0, 0],
2025-03-14T04:33:23.5668771Z             ...         [1, 1, 0, 0],
2025-03-14T04:33:23.5669288Z             ...         [0, 0, 1, 1],
2025-03-14T04:33:23.5669797Z             ...     ],
2025-03-14T04:33:23.5670222Z             ... ])
2025-03-14T04:33:23.5670616Z     
2025-03-14T04:33:23.5671209Z             >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2025-03-14T04:33:23.5672182Z             >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2025-03-14T04:33:23.5673015Z             >>> # each other, so one of them will be removed.
2025-03-14T04:33:23.5673654Z             >>> (a[0, :, :] == a[2, :, :]).all()
2025-03-14T04:33:23.5674219Z             tensor(True)
2025-03-14T04:33:23.5674740Z             >>> a_unique_dim0 = torch.unique(a, dim=0)
2025-03-14T04:33:23.5675348Z             >>> a_unique_dim0
2025-03-14T04:33:23.5675846Z             tensor([[[0, 0, 1, 1],
2025-03-14T04:33:23.5676356Z                      [0, 0, 1, 1],
2025-03-14T04:33:23.5676862Z                      [1, 1, 1, 1]],
2025-03-14T04:33:23.5677377Z                     [[1, 1, 0, 0],
2025-03-14T04:33:23.5677876Z                      [1, 1, 0, 0],
2025-03-14T04:33:23.5678377Z                      [0, 0, 1, 1]]])
2025-03-14T04:33:23.5678876Z     
2025-03-14T04:33:23.5679594Z             >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2025-03-14T04:33:23.5680395Z             >>> # `a_unique_dim0`:
2025-03-14T04:33:23.5680975Z             >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2025-03-14T04:33:23.5681569Z             tensor(True)
2025-03-14T04:33:23.5682092Z             >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2025-03-14T04:33:23.5682700Z             tensor(True)
2025-03-14T04:33:23.5683154Z     
2025-03-14T04:33:23.5683729Z             >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2025-03-14T04:33:23.5684668Z             >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2025-03-14T04:33:23.5685418Z             >>> # them will be removed.
2025-03-14T04:33:23.5685994Z             >>> (a[:, 0, :] == a[:, 1, :]).all()
2025-03-14T04:33:23.5686563Z             tensor(True)
2025-03-14T04:33:23.5687053Z             >>> torch.unique(a, dim=1)
2025-03-14T04:33:23.5687612Z             tensor([[[0, 0, 1, 1],
2025-03-14T04:33:23.5688129Z                      [1, 1, 0, 0]],
2025-03-14T04:33:23.5688644Z                     [[1, 1, 1, 1],
2025-03-14T04:33:23.5689149Z                      [0, 0, 1, 1]],
2025-03-14T04:33:23.5689670Z                     [[0, 0, 1, 1],
2025-03-14T04:33:23.5690182Z                      [1, 1, 0, 0]]])
2025-03-14T04:33:23.5690693Z     
2025-03-14T04:33:23.5691263Z             >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2025-03-14T04:33:23.5692196Z             >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2025-03-14T04:33:23.5693062Z             >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2025-03-14T04:33:23.5693819Z             >>> # sub-tensors will be removed.
2025-03-14T04:33:23.5694418Z             >>> (a[:, :, 0] == a[:, :, 1]).all()
2025-03-14T04:33:23.5694994Z             tensor(True)
2025-03-14T04:33:23.5695475Z             >>> (a[:, :, 2] == a[:, :, 3]).all()
2025-03-14T04:33:23.5696042Z             tensor(True)
2025-03-14T04:33:23.5696532Z             >>> torch.unique(a, dim=2)
2025-03-14T04:33:23.5697084Z             tensor([[[0, 1],
2025-03-14T04:33:23.5697556Z                      [0, 1],
2025-03-14T04:33:23.5698024Z                      [1, 0]],
2025-03-14T04:33:23.5698500Z                     [[1, 0],
2025-03-14T04:33:23.5698968Z                      [1, 0],
2025-03-14T04:33:23.5699437Z                      [1, 1]],
2025-03-14T04:33:23.5699918Z                     [[0, 1],
2025-03-14T04:33:23.5700387Z                      [0, 1],
2025-03-14T04:33:23.5700855Z                      [1, 0]]])
2025-03-14T04:33:23.5701315Z         
2025-03-14T04:33:23.5701986Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.5702867Z     
2025-03-14T04:33:23.5703247Z       warnings.warn(msg)
2025-03-14T04:33:23.5703704Z     
2025-03-14T04:33:23.5704293Z --- Parse Warning: 4 / 116 ---
2025-03-14T04:33:23.5706187Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560.
2025-03-14T04:33:23.5708329Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5709196Z     
2025-03-14T04:33:23.5709686Z     Load a model from a github repo or a local directory.
2025-03-14T04:33:23.5710332Z     
2025-03-14T04:33:23.5710953Z     Note: Loading a model is the typical use case, but this can also be used to
2025-03-14T04:33:23.5711983Z     for loading other objects such as tokenizers, loss functions, etc.
2025-03-14T04:33:23.5712741Z     
2025-03-14T04:33:23.5713256Z     If ``source`` is 'github', ``repo_or_dir`` is expected to be
2025-03-14T04:33:23.5714079Z     of the form ``repo_owner/repo_name[:ref]`` with an optional
2025-03-14T04:33:23.5714774Z     ref (a tag or a branch).
2025-03-14T04:33:23.5715258Z     
2025-03-14T04:33:23.5715795Z     If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2025-03-14T04:33:23.5716511Z     path to a local directory.
2025-03-14T04:33:23.5717015Z     
2025-03-14T04:33:23.5717381Z     Args:
2025-03-14T04:33:23.5717844Z         repo_or_dir (str): If ``source`` is 'github',
2025-03-14T04:33:23.5718815Z             this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2025-03-14T04:33:23.5720191Z             an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2025-03-14T04:33:23.5721454Z             the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2025-03-14T04:33:23.5722526Z             If ``source`` is 'local'  then it should be a path to a local directory.
2025-03-14T04:33:23.5723466Z         model (str): the name of a callable (entrypoint) defined in the
2025-03-14T04:33:23.5724227Z             repo/dir's ``hubconf.py``.
2025-03-14T04:33:23.5724971Z         *args (optional): the corresponding args for callable ``model``.
2025-03-14T04:33:23.5725867Z         source (str, optional): 'github' or 'local'. Specifies how
2025-03-14T04:33:23.5726707Z             ``repo_or_dir`` is to be interpreted. Default is 'github'.
2025-03-14T04:33:23.5727605Z         trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2025-03-14T04:33:23.5728635Z             This parameter was introduced in v1.12 and helps ensuring that users
2025-03-14T04:33:23.5729512Z             only run code from repos that they trust.
2025-03-14T04:33:23.5730232Z     
2025-03-14T04:33:23.5730815Z             - If ``False``, a prompt will ask the user whether the repo should
2025-03-14T04:33:23.5731552Z               be trusted.
2025-03-14T04:33:23.5732188Z             - If ``True``, the repo will be added to the trusted list and loaded
2025-03-14T04:33:23.5732997Z               without requiring explicit confirmation.
2025-03-14T04:33:23.5733752Z             - If ``"check"``, the repo will be checked against the list of
2025-03-14T04:33:23.5734664Z               trusted repos in the cache. If it is not present in that list, the
2025-03-14T04:33:23.5735599Z               behaviour will fall back onto the ``trust_repo=False`` option.
2025-03-14T04:33:23.5736511Z             - If ``None``: this will raise a warning, inviting the user to set
2025-03-14T04:33:23.5737396Z               ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2025-03-14T04:33:23.5738329Z               is only present for backward compatibility and will be removed in
2025-03-14T04:33:23.5739092Z               v2.0.
2025-03-14T04:33:23.5739501Z     
2025-03-14T04:33:23.5740088Z             Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2025-03-14T04:33:23.5741117Z         force_reload (bool, optional): whether to force a fresh download of
2025-03-14T04:33:23.5742066Z             the github repo unconditionally. Does not have any effect if
2025-03-14T04:33:23.5742852Z             ``source = 'local'``. Default is ``False``.
2025-03-14T04:33:23.5743660Z         verbose (bool, optional): If ``False``, mute messages about hitting
2025-03-14T04:33:23.5744633Z             local caches. Note that the message about first download cannot be
2025-03-14T04:33:23.5745543Z             muted. Does not have any effect if ``source = 'local'``.
2025-03-14T04:33:23.5746247Z             Default is ``True``.
2025-03-14T04:33:23.5747130Z         skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2025-03-14T04:33:23.5748414Z             specified by the ``github`` argument properly belongs to the repo owner. This will make
2025-03-14T04:33:23.5749824Z             requests to the GitHub API; you can specify a non-default GitHub token by setting the
2025-03-14T04:33:23.5750876Z             ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2025-03-14T04:33:23.5751817Z         **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-14T04:33:23.5752594Z     
2025-03-14T04:33:23.5752964Z     Returns:
2025-03-14T04:33:23.5753619Z         The output of the ``model`` callable when called with the given
2025-03-14T04:33:23.5754362Z         ``*args`` and ``**kwargs``.
2025-03-14T04:33:23.5754882Z     
2025-03-14T04:33:23.5755230Z     Example:
2025-03-14T04:33:23.5755711Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-14T04:33:23.5756345Z         >>> # from a github repo
2025-03-14T04:33:23.5756881Z         >>> repo = "pytorch/vision"
2025-03-14T04:33:23.5757440Z         >>> model = torch.hub.load(
2025-03-14T04:33:23.5758140Z         ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2025-03-14T04:33:23.5758849Z         ... )
2025-03-14T04:33:23.5759314Z         >>> # from a local directory
2025-03-14T04:33:23.5759918Z         >>> path = "/some/local/path/pytorch/vision"
2025-03-14T04:33:23.5760548Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.5761313Z         >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2025-03-14T04:33:23.5762135Z     
2025-03-14T04:33:23.5762805Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.5763635Z     
2025-03-14T04:33:23.5764018Z       warnings.warn(msg)
2025-03-14T04:33:23.5764472Z     
2025-03-14T04:33:23.5765024Z --- Parse Warning: 5 / 116 ---
2025-03-14T04:33:23.5767027Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=652.
2025-03-14T04:33:23.5769272Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5770130Z     
2025-03-14T04:33:23.5770648Z     Load a model from a local directory with a ``hubconf.py``.
2025-03-14T04:33:23.5771325Z     
2025-03-14T04:33:23.5771681Z     Args:
2025-03-14T04:33:23.5772238Z         hubconf_dir (str): path to a local directory that contains a
2025-03-14T04:33:23.5772968Z             ``hubconf.py``.
2025-03-14T04:33:23.5773619Z         model (str): name of an entrypoint defined in the directory's
2025-03-14T04:33:23.5774338Z             ``hubconf.py``.
2025-03-14T04:33:23.5775012Z         *args (optional): the corresponding args for callable ``model``.
2025-03-14T04:33:23.5775978Z         **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-14T04:33:23.5776743Z     
2025-03-14T04:33:23.5777106Z     Returns:
2025-03-14T04:33:23.5777632Z         a single model with corresponding pretrained weights.
2025-03-14T04:33:23.5778305Z     
2025-03-14T04:33:23.5778671Z     Example:
2025-03-14T04:33:23.5779128Z         >>> # xdoctest: +SKIP("stub local path")
2025-03-14T04:33:23.5779846Z         >>> path = "/some/local/path/pytorch/vision"
2025-03-14T04:33:23.5780478Z         >>> model = _load_local(
2025-03-14T04:33:23.5780988Z         ...     path,
2025-03-14T04:33:23.5781433Z         ...     "resnet50",
2025-03-14T04:33:23.5782005Z         ...     weights="ResNet50_Weights.IMAGENET1K_V1",
2025-03-14T04:33:23.5782624Z         ... )
2025-03-14T04:33:23.5783010Z     
2025-03-14T04:33:23.5783681Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.5784519Z     
2025-03-14T04:33:23.5784902Z       warnings.warn(msg)
2025-03-14T04:33:23.5785361Z     
2025-03-14T04:33:23.5785899Z --- Parse Warning: 6 / 116 ---
2025-03-14T04:33:23.5787890Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=691.
2025-03-14T04:33:23.5790151Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5791127Z     Download object at the given URL to a local path.
2025-03-14T04:33:23.5791756Z     
2025-03-14T04:33:23.5792116Z         Args:
2025-03-14T04:33:23.5792583Z             url (str): URL of the object to download
2025-03-14T04:33:23.5793471Z             dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2025-03-14T04:33:23.5794732Z             hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2025-03-14T04:33:23.5795732Z                 Default: None
2025-03-14T04:33:23.5796513Z             progress (bool, optional): whether or not to display a progress bar to stderr
2025-03-14T04:33:23.5797369Z                 Default: True
2025-03-14T04:33:23.5797844Z     
2025-03-14T04:33:23.5798220Z         Example:
2025-03-14T04:33:23.5798712Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-14T04:33:23.5799453Z             >>> # xdoctest: +REQUIRES(POSIX)
2025-03-14T04:33:23.5800074Z             >>> torch.hub.download_url_to_file(
2025-03-14T04:33:23.5800889Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2025-03-14T04:33:23.5801706Z             ...     "/tmp/temporary_file",
2025-03-14T04:33:23.5802262Z             ... )
2025-03-14T04:33:23.5802668Z     
2025-03-14T04:33:23.5803027Z         
2025-03-14T04:33:23.5803702Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.5804536Z     
2025-03-14T04:33:23.5804919Z       warnings.warn(msg)
2025-03-14T04:33:23.5805369Z     
2025-03-14T04:33:23.5805903Z --- Parse Warning: 7 / 116 ---
2025-03-14T04:33:23.5807971Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=816.
2025-03-14T04:33:23.5810331Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5811319Z     Loads the Torch serialized object at the given URL.
2025-03-14T04:33:23.5811947Z     
2025-03-14T04:33:23.5812492Z         If downloaded file is a zip file, it will be automatically
2025-03-14T04:33:23.5813200Z         decompressed.
2025-03-14T04:33:23.5813649Z     
2025-03-14T04:33:23.5814250Z         If the object is already present in `model_dir`, it's deserialized and
2025-03-14T04:33:23.5815026Z         returned.
2025-03-14T04:33:23.5815669Z         The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2025-03-14T04:33:23.5816628Z         ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2025-03-14T04:33:23.5817363Z     
2025-03-14T04:33:23.5817733Z         Args:
2025-03-14T04:33:23.5818206Z             url (str): URL of the object to download
2025-03-14T04:33:23.5819010Z             model_dir (str, optional): directory in which to save the object
2025-03-14T04:33:23.5820275Z             map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2025-03-14T04:33:23.5821605Z             progress (bool, optional): whether or not to display a progress bar to stderr.
2025-03-14T04:33:23.5822472Z                 Default: True
2025-03-14T04:33:23.5823399Z             check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2025-03-14T04:33:23.5824608Z                 ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2025-03-14T04:33:23.5825646Z                 digits of the SHA256 hash of the contents of the file. The hash is used to
2025-03-14T04:33:23.5826631Z                 ensure unique names and to verify the contents of the file.
2025-03-14T04:33:23.5827383Z                 Default: False
2025-03-14T04:33:23.5828332Z             file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2025-03-14T04:33:23.5829758Z             weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2025-03-14T04:33:23.5831039Z                 Recommended for untrusted sources. See :func:`~torch.load` for more details.
2025-03-14T04:33:23.5831884Z     
2025-03-14T04:33:23.5832237Z         Example:
2025-03-14T04:33:23.5832766Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-14T04:33:23.5833492Z             >>> state_dict = torch.hub.load_state_dict_from_url(
2025-03-14T04:33:23.5834359Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2025-03-14T04:33:23.5835121Z             ... )
2025-03-14T04:33:23.5835523Z     
2025-03-14T04:33:23.5835878Z         
2025-03-14T04:33:23.5836550Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.5837383Z     
2025-03-14T04:33:23.5837774Z       warnings.warn(msg)
2025-03-14T04:33:23.5838234Z     
2025-03-14T04:33:23.5838775Z --- Parse Warning: 8 / 116 ---
2025-03-14T04:33:23.5840857Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=376.
2025-03-14T04:33:23.5843108Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:23.5844219Z     Registers the function implementation as the fallback for the given key.
2025-03-14T04:33:23.5845041Z     
2025-03-14T04:33:23.5845641Z             This function only works for a library with global namespace ("_").
2025-03-14T04:33:23.5846414Z     
2025-03-14T04:33:23.5846766Z             Args:
2025-03-14T04:33:23.5847523Z                 fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2025-03-14T04:33:23.5848509Z                     to register a fallthrough.
2025-03-14T04:33:23.5849688Z                 dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2025-03-14T04:33:23.5850823Z                               the dispatch key that the library was created with.
2025-03-14T04:33:23.5852034Z                 with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2025-03-14T04:33:23.5853535Z                              to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2025-03-14T04:33:23.5854512Z     
2025-03-14T04:33:23.5854904Z             Example::
2025-03-14T04:33:23.5855388Z                 >>> my_lib = Library("_", "IMPL")
2025-03-14T04:33:23.5856046Z                 >>> def fallback_kernel(op, *args, **kwargs):
2025-03-14T04:33:23.5856737Z                 >>>     # Handle all autocast ops generically
2025-03-14T04:33:23.5857354Z                 >>>     # ...
2025-03-14T04:33:23.5857935Z                 >>> my_lib.fallback(fallback_kernel, "Autocast")
2025-03-14T04:33:23.5858577Z             
2025-03-14T04:33:23.5859979Z     Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2025-03-14T04:33:23.5861597Z     
2025-03-14T04:33:23.5862051Z     my_lib.fallback(fallback_kernel, "Autocast")
2025-03-14T04:33:23.5862663Z     ^
2025-03-14T04:33:23.5863057Z       warnings.warn(msg)
2025-03-14T04:33:23.5863523Z     
2025-03-14T04:33:23.5864072Z --- Parse Warning: 9 / 116 ---
2025-03-14T04:33:23.5866058Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=920.
2025-03-14T04:33:23.5868274Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:23.5869369Z     Register a FakeTensor implementation ("fake impl") for this operator.
2025-03-14T04:33:23.5870136Z     
2025-03-14T04:33:23.5870657Z         Also sometimes known as a "meta kernel", "abstract impl".
2025-03-14T04:33:23.5871346Z     
2025-03-14T04:33:23.5871988Z         An "FakeTensor implementation" specifies the behavior of this operator on
2025-03-14T04:33:23.5873062Z         Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2025-03-14T04:33:23.5874180Z         certain properties (sizes/strides/storage_offset/device), it specifies
2025-03-14T04:33:23.5875089Z         what the properties of the output Tensors are.
2025-03-14T04:33:23.5875704Z     
2025-03-14T04:33:23.5876330Z         The FakeTensor implementation has the same signature as the operator.
2025-03-14T04:33:23.5877351Z         It is run for both FakeTensors and meta tensors. To write a FakeTensor
2025-03-14T04:33:23.5878349Z         implementation, assume that all Tensor inputs to the operator are
2025-03-14T04:33:23.5879400Z         regular CPU/CUDA/Meta tensors, but they do not have storage, and
2025-03-14T04:33:23.5880368Z         you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2025-03-14T04:33:23.5881387Z         The FakeTensor implementation must consist of only PyTorch operations
2025-03-14T04:33:23.5882386Z         (and may not directly access the storage or data of any input or
2025-03-14T04:33:23.5883139Z         intermediate Tensors).
2025-03-14T04:33:23.5883628Z     
2025-03-14T04:33:23.5884111Z         This API may be used as a decorator (see examples).
2025-03-14T04:33:23.5927974Z     
2025-03-14T04:33:23.5928568Z         For a detailed guide on custom ops, please see
2025-03-14T04:33:23.5929444Z         https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2025-03-14T04:33:23.5930217Z     
2025-03-14T04:33:23.5930576Z         Examples:
2025-03-14T04:33:23.5931018Z             >>> import torch
2025-03-14T04:33:23.5931533Z             >>> import numpy as np
2025-03-14T04:33:23.5932334Z             >>> from torch import Tensor
2025-03-14T04:33:23.5932888Z             >>>
2025-03-14T04:33:23.5933474Z             >>> # Example 1: an operator without data-dependent output shape
2025-03-14T04:33:23.5934443Z             >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2025-03-14T04:33:23.5935453Z             >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-03-14T04:33:23.5936408Z             >>>     raise NotImplementedError("Implementation goes here")
2025-03-14T04:33:23.5937097Z             >>>
2025-03-14T04:33:23.5937644Z             >>> @torch.library.register_fake("mylib::custom_linear")
2025-03-14T04:33:23.5938349Z             >>> def _(x, weight, bias):
2025-03-14T04:33:23.5938919Z             >>>     assert x.dim() == 2
2025-03-14T04:33:23.5939500Z             >>>     assert weight.dim() == 2
2025-03-14T04:33:23.5940091Z             >>>     assert bias.dim() == 1
2025-03-14T04:33:23.5940709Z             >>>     assert x.shape[1] == weight.shape[1]
2025-03-14T04:33:23.5941383Z             >>>     assert weight.shape[0] == bias.shape[0]
2025-03-14T04:33:23.5942046Z             >>>     assert x.device == weight.device
2025-03-14T04:33:23.5942681Z             >>>
2025-03-14T04:33:23.5943150Z             >>>     return (x @ weight.t()) + bias
2025-03-14T04:33:23.5943725Z             >>>
2025-03-14T04:33:23.5944273Z             >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2025-03-14T04:33:23.5945004Z             >>>     x = torch.randn(2, 3)
2025-03-14T04:33:23.5945584Z             >>>     w = torch.randn(3, 3)
2025-03-14T04:33:23.5946154Z             >>>     b = torch.randn(3)
2025-03-14T04:33:23.5946775Z             >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2025-03-14T04:33:23.5947397Z             >>>
2025-03-14T04:33:23.5947811Z             >>> assert y.shape == (2, 3)
2025-03-14T04:33:23.5948360Z             >>>
2025-03-14T04:33:23.5949078Z             >>> # Example 2: an operator with data-dependent output shape
2025-03-14T04:33:23.5950029Z             >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2025-03-14T04:33:23.5950893Z             >>> def custom_nonzero(x: Tensor) -> Tensor:
2025-03-14T04:33:23.5951553Z             >>>     x_np = x.numpy(force=True)
2025-03-14T04:33:23.5952203Z             >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-03-14T04:33:23.5952896Z             >>>     return torch.tensor(res, device=x.device)
2025-03-14T04:33:23.5953510Z             >>>
2025-03-14T04:33:23.5954137Z             >>> @torch.library.register_fake("mylib::custom_nonzero")
2025-03-14T04:33:23.5954842Z             >>> def _(x):
2025-03-14T04:33:23.5955410Z             >>> # Number of nonzero-elements is data-dependent.
2025-03-14T04:33:23.5956172Z             >>> # Since we cannot peek at the data in an fake impl,
2025-03-14T04:33:23.5956949Z             >>> # we use the ctx object to construct a new symint that
2025-03-14T04:33:23.5957682Z             >>> # represents the data-dependent size.
2025-03-14T04:33:23.5958335Z             >>>     ctx = torch.library.get_ctx()
2025-03-14T04:33:23.5958970Z             >>>     nnz = ctx.new_dynamic_size()
2025-03-14T04:33:23.5959664Z             >>>     shape = [nnz, x.dim()]
2025-03-14T04:33:23.5960324Z             >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-03-14T04:33:23.5960996Z             >>>     return result
2025-03-14T04:33:23.5961508Z             >>>
2025-03-14T04:33:23.5962064Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-14T04:33:23.5962768Z             >>>
2025-03-14T04:33:23.5963224Z             >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2025-03-14T04:33:23.5964073Z             >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2025-03-14T04:33:23.5964943Z             >>> trace.print_readable()
2025-03-14T04:33:23.5965483Z             >>>
2025-03-14T04:33:23.5966125Z             >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2025-03-14T04:33:23.5967020Z     
2025-03-14T04:33:23.5967381Z         
2025-03-14T04:33:23.5968571Z     Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2025-03-14T04:33:23.5969911Z     
2025-03-14T04:33:23.5970265Z     _._ = None
2025-03-14T04:33:23.5970658Z     ^
2025-03-14T04:33:23.5971044Z       warnings.warn(msg)
2025-03-14T04:33:23.5971505Z     
2025-03-14T04:33:23.5972140Z --- Parse Warning: 10 / 116 ---
2025-03-14T04:33:23.5974181Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1041.
2025-03-14T04:33:23.5976470Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.5977437Z     Register a backward formula for this custom op.
2025-03-14T04:33:23.5978067Z     
2025-03-14T04:33:23.5978635Z         In order for an operator to work with autograd, you need to register
2025-03-14T04:33:23.5979411Z         a backward formula:
2025-03-14T04:33:23.5980156Z         1. You must tell us how to compute gradients during the backward pass
2025-03-14T04:33:23.5980955Z         by providing us a "backward" function.
2025-03-14T04:33:23.5981765Z         2. If you need any values from the forward to compute gradients, you can
2025-03-14T04:33:23.5982631Z         use `setup_context` to save values for backward.
2025-03-14T04:33:23.5983259Z     
2025-03-14T04:33:23.5983876Z         ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2025-03-14T04:33:23.5984854Z         - ``grads`` is one or more gradients. The number of gradients matches
2025-03-14T04:33:23.5985650Z         the number of outputs of the operator.
2025-03-14T04:33:23.5986466Z         The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2025-03-14T04:33:23.5987527Z         :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2025-03-14T04:33:23.5988471Z         same as :meth:`torch.autograd.Function.backward`.
2025-03-14T04:33:23.5989124Z     
2025-03-14T04:33:23.5989716Z         ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2025-03-14T04:33:23.5990729Z         Please save quantities needed for backward onto the ``ctx`` object via
2025-03-14T04:33:23.5991788Z         either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2025-03-14T04:33:23.5992835Z         or assigning them as attributes of ``ctx``. If your custom op has
2025-03-14T04:33:23.5993812Z         kwarg-only arguments, we expect the signature of ``setup_context``
2025-03-14T04:33:23.5994767Z         to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2025-03-14T04:33:23.5995489Z     
2025-03-14T04:33:23.5996098Z         Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2025-03-14T04:33:23.5997143Z         they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2025-03-14T04:33:23.5998192Z         not depend on or mutate global state. If you need a non-traceable backward,
2025-03-14T04:33:23.5999300Z         you can make it a separate custom_op that you call inside ``backward_fn``.
2025-03-14T04:33:23.6000087Z     
2025-03-14T04:33:23.6000699Z         If you need different autograd behavior on different devices, then we
2025-03-14T04:33:23.6001747Z         recommend creating two different custom operators, one for each device
2025-03-14T04:33:23.6002819Z         that needs different behavior, and switching between them at runtime.
2025-03-14T04:33:23.6003589Z     
2025-03-14T04:33:23.6003960Z         Examples:
2025-03-14T04:33:23.6004384Z             >>> import torch
2025-03-14T04:33:23.6004886Z             >>> import numpy as np
2025-03-14T04:33:23.6005442Z             >>> from torch import Tensor
2025-03-14T04:33:23.6005989Z             >>>
2025-03-14T04:33:23.6006637Z             >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2025-03-14T04:33:23.6007446Z             >>> def numpy_sin(x: Tensor) -> Tensor:
2025-03-14T04:33:23.6008047Z             >>>     x_np = x.cpu().numpy()
2025-03-14T04:33:23.6008597Z             >>>     y_np = np.sin(x_np)
2025-03-14T04:33:23.6009241Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-14T04:33:23.6009888Z             >>>
2025-03-14T04:33:23.6010404Z             >>> def setup_context(ctx, inputs, output) -> Tensor:
2025-03-14T04:33:23.6011029Z             >>>     x, = inputs
2025-03-14T04:33:23.6011543Z             >>>     ctx.save_for_backward(x)
2025-03-14T04:33:23.6012076Z             >>>
2025-03-14T04:33:23.6012503Z             >>> def backward(ctx, grad):
2025-03-14T04:33:23.6013063Z             >>>     x, = ctx.saved_tensors
2025-03-14T04:33:23.6013617Z             >>>     return grad * x.cos()
2025-03-14T04:33:23.6014139Z             >>>
2025-03-14T04:33:23.6014605Z             >>> torch.library.register_autograd(
2025-03-14T04:33:23.6015359Z             ...     "mylib::numpy_sin", backward, setup_context=setup_context
2025-03-14T04:33:23.6016063Z             ... )
2025-03-14T04:33:23.6016434Z             >>>
2025-03-14T04:33:23.6016921Z             >>> x = torch.randn(3, requires_grad=True)
2025-03-14T04:33:23.6017525Z             >>> y = numpy_sin(x)
2025-03-14T04:33:23.6018160Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-14T04:33:23.6018894Z             >>> assert torch.allclose(grad_x, x.cos())
2025-03-14T04:33:23.6019466Z             >>>
2025-03-14T04:33:23.6019908Z             >>> # Example with a keyword-only arg
2025-03-14T04:33:23.6020692Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-14T04:33:23.6021534Z             >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2025-03-14T04:33:23.6022177Z             >>>     x_np = x.cpu().numpy()
2025-03-14T04:33:23.6022748Z             >>>     y_np = x_np * val
2025-03-14T04:33:23.6023388Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-14T04:33:23.6024048Z             >>>
2025-03-14T04:33:23.6024686Z             >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2025-03-14T04:33:23.6025545Z             >>>     ctx.val = keyword_only_inputs["val"]
2025-03-14T04:33:23.6026141Z             >>>
2025-03-14T04:33:23.6026569Z             >>> def backward(ctx, grad):
2025-03-14T04:33:23.6027127Z             >>>     return grad * ctx.val
2025-03-14T04:33:23.6027690Z             >>>
2025-03-14T04:33:23.6028130Z             >>> torch.library.register_autograd(
2025-03-14T04:33:23.6028878Z             ...     "mylib::numpy_mul", backward, setup_context=setup_context
2025-03-14T04:33:23.6029559Z             ... )
2025-03-14T04:33:23.6029936Z             >>>
2025-03-14T04:33:23.6030377Z             >>> x = torch.randn(3, requires_grad=True)
2025-03-14T04:33:23.6031007Z             >>> y = numpy_mul(x, val=3.14)
2025-03-14T04:33:23.6031685Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-14T04:33:23.6032519Z             >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2025-03-14T04:33:23.6033195Z     
2025-03-14T04:33:23.6033532Z         
2025-03-14T04:33:23.6034183Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6034994Z     
2025-03-14T04:33:23.6035368Z       warnings.warn(msg)
2025-03-14T04:33:23.6035828Z     
2025-03-14T04:33:23.6036390Z --- Parse Warning: 11 / 116 ---
2025-03-14T04:33:23.6038318Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1455.
2025-03-14T04:33:23.6040600Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6041683Z     Given an operator and some sample arguments, tests if the operator is
2025-03-14T04:33:23.6042468Z         registered correctly.
2025-03-14T04:33:23.6043060Z     
2025-03-14T04:33:23.6043617Z         That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2025-03-14T04:33:23.6044636Z         custom op, you specified metadata (e.g. mutability info) about the custom op
2025-03-14T04:33:23.6045690Z         and these APIs require that the functions you pass them satisfy certain
2025-03-14T04:33:23.6046728Z         properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2025-03-14T04:33:23.6047625Z         ``opcheck`` tests these metadata and properties.
2025-03-14T04:33:23.6048236Z     
2025-03-14T04:33:23.6048643Z         Concretely, we test the following:
2025-03-14T04:33:23.6049323Z     
2025-03-14T04:33:23.6049841Z         - test_schema: If the schema matches the implementation of
2025-03-14T04:33:23.6050780Z           the operator. For example: if the schema specifies a Tensor is mutated,
2025-03-14T04:33:23.6051789Z           then we check the implementation mutates the Tensor. If the schema
2025-03-14T04:33:23.6052734Z           specifies that we return a new Tensor, then we check that the
2025-03-14T04:33:23.6053699Z           implementation returns a new Tensor (instead of an existing one or
2025-03-14T04:33:23.6054610Z           a view of an existing one).
2025-03-14T04:33:23.6055360Z         - test_autograd_registration: If the operator supports training
2025-03-14T04:33:23.6056292Z           (autograd): we check that its autograd formula is registered via
2025-03-14T04:33:23.6057263Z           torch.library.register_autograd or a manual registration to one
2025-03-14T04:33:23.6058235Z           or more DispatchKey::Autograd keys. Any other DispatchKey-based
2025-03-14T04:33:23.6059070Z           registrations may lead to undefined behavior.
2025-03-14T04:33:23.6059851Z         - test_faketensor: If the operator has a FakeTensor kernel
2025-03-14T04:33:23.6060686Z           (and if it is correct). The FakeTensor kernel is necessary (
2025-03-14T04:33:23.6061626Z           but not sufficient) for the operator to work with PyTorch compilation
2025-03-14T04:33:23.6062640Z           APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2025-03-14T04:33:23.6063595Z           (also sometimes known as a meta kernel) was registered for the
2025-03-14T04:33:23.6064510Z           operator and that it is correct. This test takes the result of
2025-03-14T04:33:23.6065429Z           running the operator on real tensors and the result of running
2025-03-14T04:33:23.6066391Z           the operator on FakeTensors and checks that they have the same
2025-03-14T04:33:23.6067239Z           Tensor metadata (sizes/strides/dtype/device/etc).
2025-03-14T04:33:23.6068091Z         - test_aot_dispatch_dynamic: If the operator has correct behavior
2025-03-14T04:33:23.6069001Z           with PyTorch compilation APIs (torch.compile/export/FX).
2025-03-14T04:33:23.6069925Z           This checks that the outputs (and gradients, if applicable) are the
2025-03-14T04:33:23.6070799Z           same under eager-mode PyTorch and torch.compile.
2025-03-14T04:33:23.6071622Z           This test is a superset of ``test_faketensor`` and is an e2e test;
2025-03-14T04:33:23.6072486Z           other things it tests are that the operator supports
2025-03-14T04:33:23.6073383Z           functionalization and that the backward pass (if it exists) also
2025-03-14T04:33:23.6074242Z           supports FakeTensor and functionalization.
2025-03-14T04:33:23.6074836Z     
2025-03-14T04:33:23.6075399Z         For best results, please call ``opcheck`` multiple times with a
2025-03-14T04:33:23.6076302Z         representative set of inputs. If your operator supports
2025-03-14T04:33:23.6077260Z         autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2025-03-14T04:33:23.6078304Z         if your operator supports multiple devices (e.g. CPU and CUDA), please
2025-03-14T04:33:23.6079268Z         use ``opcheck`` with inputs on all supported devices.
2025-03-14T04:33:23.6079923Z     
2025-03-14T04:33:23.6080283Z         Args:
2025-03-14T04:33:23.6080938Z             op: The operator. Must either be a function decorated with
2025-03-14T04:33:23.6081860Z                 :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2025-03-14T04:33:23.6082854Z                 found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2025-03-14T04:33:23.6083663Z             args: The args to the operator
2025-03-14T04:33:23.6084264Z             kwargs: The kwargs to the operator
2025-03-14T04:33:23.6084994Z             test_utils: Tests that we should run. Default: all of them.
2025-03-14T04:33:23.6085781Z                 Example: ("test_schema", "test_faketensor")
2025-03-14T04:33:23.6086594Z             raise_exception: If we should raise an exception on the first
2025-03-14T04:33:23.6087465Z                 error. If False, we will return a dict with information
2025-03-14T04:33:23.6088191Z                 on if each test passed or not.
2025-03-14T04:33:23.6089035Z             rtol (Optional[float]): Relative tolerance for floating point comparisons.
2025-03-14T04:33:23.6089936Z                 If specified ``atol`` must also be specified.
2025-03-14T04:33:23.6090744Z                 If omitted, default values based on the ``dtype`` are selected
2025-03-14T04:33:23.6091640Z                 (see the table in :func:`torch.testing.assert_close`).
2025-03-14T04:33:23.6092587Z             atol (Optional[float]): Absolute tolerance for floating point comparisons.
2025-03-14T04:33:23.6093502Z                 If specified ``rtol`` must also be specified.
2025-03-14T04:33:23.6094309Z                 If omitted, default values based on the ``dtype`` are selected
2025-03-14T04:33:23.6095172Z                 (see the table in :func:`torch.testing.assert_close`).
2025-03-14T04:33:23.6095848Z     
2025-03-14T04:33:23.6096222Z         .. warning::
2025-03-14T04:33:23.6096645Z     
2025-03-14T04:33:23.6097234Z             opcheck and :func:`torch.autograd.gradcheck` test different things;
2025-03-14T04:33:23.6098225Z             opcheck tests if your usage of torch.library APIs is correct while
2025-03-14T04:33:23.6099215Z             :func:`torch.autograd.gradcheck` tests if your autograd formula is
2025-03-14T04:33:23.6099607Z             mathematically correct. Use both to test custom ops that support
2025-03-14T04:33:23.6099813Z             gradient computation.
2025-03-14T04:33:23.6099965Z     
2025-03-14T04:33:23.6100146Z         Example:
2025-03-14T04:33:23.6100293Z     
2025-03-14T04:33:23.6100594Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:23.6100960Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-14T04:33:23.6101220Z             >>> def numpy_mul(x: Tensor, y: float) -> Tensor:
2025-03-14T04:33:23.6101422Z             >>>     x_np = x.numpy(force=True)
2025-03-14T04:33:23.6101613Z             >>>     z_np = x_np * y
2025-03-14T04:33:23.6101855Z             >>>     return torch.from_numpy(z_np).to(x.device)
2025-03-14T04:33:23.6102020Z             >>>
2025-03-14T04:33:23.6102221Z             >>> @numpy_mul.register_fake
2025-03-14T04:33:23.6102401Z             >>> def _(x, y):
2025-03-14T04:33:23.6102600Z             >>>     return torch.empty_like(x)
2025-03-14T04:33:23.6102763Z             >>>
2025-03-14T04:33:23.6102988Z             >>> def setup_context(ctx, inputs, output):
2025-03-14T04:33:23.6103164Z             >>>     y, = inputs
2025-03-14T04:33:23.6103324Z             >>>     ctx.y = y
2025-03-14T04:33:23.6103474Z             >>>
2025-03-14T04:33:23.6103681Z             >>> def backward(ctx, grad):
2025-03-14T04:33:23.6103874Z             >>>     return grad * ctx.y, None
2025-03-14T04:33:23.6104037Z             >>>
2025-03-14T04:33:23.6104435Z             >>> numpy_mul.register_autograd(backward, setup_context=setup_context)
2025-03-14T04:33:23.6104601Z             >>>
2025-03-14T04:33:23.6104778Z             >>> sample_inputs = [
2025-03-14T04:33:23.6104980Z             >>>     (torch.randn(3), 3.14),
2025-03-14T04:33:23.6105247Z             >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2025-03-14T04:33:23.6105557Z             >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2025-03-14T04:33:23.6105892Z             >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2025-03-14T04:33:23.6106059Z             >>> ]
2025-03-14T04:33:23.6106214Z             >>>
2025-03-14T04:33:23.6106423Z             >>> for args in sample_inputs:
2025-03-14T04:33:23.6106665Z             >>>     torch.library.opcheck(numpy_mul, args)
2025-03-14T04:33:23.6106827Z     
2025-03-14T04:33:23.6106971Z         
2025-03-14T04:33:23.6107440Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6107586Z     
2025-03-14T04:33:23.6107774Z       warnings.warn(msg)
2025-03-14T04:33:23.6107924Z     
2025-03-14T04:33:23.6108320Z --- Parse Warning: 12 / 116 ---
2025-03-14T04:33:23.6109873Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1283.
2025-03-14T04:33:23.6110364Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6110998Z     load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args)
2025-03-14T04:33:23.6111158Z     
2025-03-14T04:33:23.6111477Z         Loads an object saved with :func:`torch.save` from a file.
2025-03-14T04:33:23.6111644Z     
2025-03-14T04:33:23.6112054Z         :func:`torch.load` uses Python's unpickling facilities but treats storages,
2025-03-14T04:33:23.6112484Z         which underlie tensors, specially. They are first deserialized on the
2025-03-14T04:33:23.6112874Z         CPU and are then moved to the device they were saved from. If this fails
2025-03-14T04:33:23.6113313Z         (e.g. because the run time system doesn't have certain devices), an exception
2025-03-14T04:33:23.6113739Z         is raised. However, storages can be dynamically remapped to an alternative
2025-03-14T04:33:23.6114057Z         set of devices using the :attr:`map_location` argument.
2025-03-14T04:33:23.6114210Z     
2025-03-14T04:33:23.6114660Z         If :attr:`map_location` is a callable, it will be called once for each serialized
2025-03-14T04:33:23.6115058Z         storage with two arguments: storage and location. The storage argument
2025-03-14T04:33:23.6115530Z         will be the initial deserialization of the storage, residing on the CPU.
2025-03-14T04:33:23.6115920Z         Each serialized storage has a location tag associated with it which
2025-03-14T04:33:23.6116318Z         identifies the device it was saved from, and this tag is the second
2025-03-14T04:33:23.6116769Z         argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2025-03-14T04:33:23.6117187Z         for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2025-03-14T04:33:23.6117554Z         :attr:`map_location` should return either ``None`` or a storage. If
2025-03-14T04:33:23.6118034Z         :attr:`map_location` returns a storage, it will be used as the final deserialized
2025-03-14T04:33:23.6118478Z         object, already moved to the right device. Otherwise, :func:`torch.load` will
2025-03-14T04:33:23.6118933Z         fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2025-03-14T04:33:23.6119083Z     
2025-03-14T04:33:23.6119588Z         If :attr:`map_location` is a :class:`torch.device` object or a string containing
2025-03-14T04:33:23.6120011Z         a device tag, it indicates the location where all tensors should be loaded.
2025-03-14T04:33:23.6120181Z     
2025-03-14T04:33:23.6120652Z         Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2025-03-14T04:33:23.6121040Z         appearing in the file (keys), to ones that specify where to put the
2025-03-14T04:33:23.6121218Z         storages (values).
2025-03-14T04:33:23.6121387Z     
2025-03-14T04:33:23.6121871Z         User extensions can register their own location tags and tagging and
2025-03-14T04:33:23.6122359Z         deserialization methods using :func:`torch.serialization.register_package`.
2025-03-14T04:33:23.6122515Z     
2025-03-14T04:33:23.6122686Z         Args:
2025-03-14T04:33:23.6123265Z             f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2025-03-14T04:33:23.6123595Z                 or a string or os.PathLike object containing a file name
2025-03-14T04:33:23.6124179Z             map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2025-03-14T04:33:23.6124360Z                 locations
2025-03-14T04:33:23.6124772Z             pickle_module: module used for unpickling metadata and objects (has to
2025-03-14T04:33:23.6125091Z                 match the :attr:`pickle_module` used to serialize file)
2025-03-14T04:33:23.6125477Z             weights_only: Indicates whether unpickler should be restricted to
2025-03-14T04:33:23.6125784Z                 loading only tensors, primitive types, dictionaries
2025-03-14T04:33:23.6126179Z                 and any types added via :func:`torch.serialization.add_safe_globals`.
2025-03-14T04:33:23.6126466Z                 See :ref:`weights-only` for more details.
2025-03-14T04:33:23.6127072Z             mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory.
2025-03-14T04:33:23.6127712Z                 Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2025-03-14T04:33:23.6128333Z                 are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2025-03-14T04:33:23.6128945Z                 second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2025-03-14T04:33:23.6129379Z                 tensor storages from disk to CPU memory in the first step, ``f`` is mmaped.
2025-03-14T04:33:23.6129832Z             pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2025-03-14T04:33:23.6130229Z                 :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2025-03-14T04:33:23.6130435Z                 :attr:`errors=...`.
2025-03-14T04:33:23.6130585Z     
2025-03-14T04:33:23.6130763Z         .. warning::
2025-03-14T04:33:23.6131145Z             :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2025-03-14T04:33:23.6131647Z             uses ``pickle`` module implicitly, which is known to be insecure.
2025-03-14T04:33:23.6132145Z             It is possible to construct malicious pickle data which will execute arbitrary code
2025-03-14T04:33:23.6132597Z             during unpickling. Never load data that could have come from an untrusted
2025-03-14T04:33:23.6133129Z             source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2025-03-14T04:33:23.6133296Z     
2025-03-14T04:33:23.6133459Z         .. note::
2025-03-14T04:33:23.6133938Z             When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2025-03-14T04:33:23.6134404Z             will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2025-03-14T04:33:23.6134903Z             and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2025-03-14T04:33:23.6135049Z     
2025-03-14T04:33:23.6135224Z         .. note::
2025-03-14T04:33:23.6135669Z             By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2025-03-14T04:33:23.6136069Z             case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2025-03-14T04:33:23.6136443Z             when loading files saved by Python 2 in Python 3.  If this default
2025-03-14T04:33:23.6136917Z             is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2025-03-14T04:33:23.6137376Z             these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2025-03-14T04:33:23.6137851Z             to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2025-03-14T04:33:23.6138261Z             as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2025-03-14T04:33:23.6138428Z     
2025-03-14T04:33:23.6138585Z         Example:
2025-03-14T04:33:23.6138833Z             >>> # xdoctest: +SKIP("undefined filepaths")
2025-03-14T04:33:23.6139080Z             >>> torch.load("tensors.pt", weights_only=True)
2025-03-14T04:33:23.6139295Z             # Load all tensors onto the CPU
2025-03-14T04:33:23.6139464Z             >>> torch.load(
2025-03-14T04:33:23.6139655Z             ...     "tensors.pt",
2025-03-14T04:33:23.6139879Z             ...     map_location=torch.device("cpu"),
2025-03-14T04:33:23.6140085Z             ...     weights_only=True,
2025-03-14T04:33:23.6140236Z             ... )
2025-03-14T04:33:23.6140511Z             # Load all tensors onto the CPU, using a function
2025-03-14T04:33:23.6140683Z             >>> torch.load(
2025-03-14T04:33:23.6140870Z             ...     "tensors.pt",
2025-03-14T04:33:23.6141157Z             ...     map_location=lambda storage, loc: storage,
2025-03-14T04:33:23.6141356Z             ...     weights_only=True,
2025-03-14T04:33:23.6141510Z             ... )
2025-03-14T04:33:23.6141722Z             # Load all tensors onto GPU 1
2025-03-14T04:33:23.6141891Z             >>> torch.load(
2025-03-14T04:33:23.6142084Z             ...     "tensors.pt",
2025-03-14T04:33:23.6142372Z             ...     map_location=lambda storage, loc: storage.cuda(1),
2025-03-14T04:33:23.6142569Z             ...     weights_only=True,
2025-03-14T04:33:23.6142772Z             ... )  # type: ignore[attr-defined]
2025-03-14T04:33:23.6142977Z             # Map tensors from GPU 1 to GPU 0
2025-03-14T04:33:23.6143163Z             >>> torch.load(
2025-03-14T04:33:23.6143340Z             ...     "tensors.pt",
2025-03-14T04:33:23.6143574Z             ...     map_location={"cuda:1": "cuda:0"},
2025-03-14T04:33:23.6143759Z             ...     weights_only=True,
2025-03-14T04:33:23.6143932Z             ... )
2025-03-14T04:33:23.6144151Z             # Load tensor from io.BytesIO object
2025-03-14T04:33:23.6144623Z             # Loading from a buffer setting weights_only=False, warning this can be unsafe
2025-03-14T04:33:23.6144838Z             >>> with open("tensor.pt", "rb") as f:
2025-03-14T04:33:23.6145054Z             ...     buffer = io.BytesIO(f.read())
2025-03-14T04:33:23.6145314Z             >>> torch.load(buffer, weights_only=False)
2025-03-14T04:33:23.6145603Z             # Load a module with 'ascii' encoding for unpickling
2025-03-14T04:33:23.6146051Z             # Loading from a module setting weights_only=False, warning this can be unsafe
2025-03-14T04:33:23.6146417Z             >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2025-03-14T04:33:23.6146571Z         
2025-03-14T04:33:23.6147048Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6147203Z     
2025-03-14T04:33:23.6147398Z       warnings.warn(msg)
2025-03-14T04:33:23.6147550Z     
2025-03-14T04:33:23.6147935Z --- Parse Warning: 13 / 116 ---
2025-03-14T04:33:23.6149781Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=38.
2025-03-14T04:33:23.6150252Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:23.6150711Z     Check if the current accelerator is available at runtime: it was build, all the
2025-03-14T04:33:23.6151114Z         required drivers are available and at least one device is visible.
2025-03-14T04:33:23.6151377Z         See :ref:`accelerator<accelerators>` for details.
2025-03-14T04:33:23.6151547Z     
2025-03-14T04:33:23.6151706Z         Returns:
2025-03-14T04:33:23.6152305Z             bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2025-03-14T04:33:23.6152504Z     
2025-03-14T04:33:23.6152683Z         Example::
2025-03-14T04:33:23.6152841Z     
2025-03-14T04:33:23.6153342Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:23.6153502Z         
2025-03-14T04:33:23.6154543Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2025-03-14T04:33:23.6154699Z     
2025-03-14T04:33:23.6155186Z     assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:23.6155362Z                                             ^
2025-03-14T04:33:23.6155553Z       warnings.warn(msg)
2025-03-14T04:33:23.6155703Z     
2025-03-14T04:33:23.6156056Z --- Parse Warning: 14 / 116 ---
2025-03-14T04:33:23.6157731Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=153.
2025-03-14T04:33:23.6158250Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:23.6158621Z     Wait for all kernels in all streams on the given device to complete.
2025-03-14T04:33:23.6158791Z     
2025-03-14T04:33:23.6158944Z         Args:
2025-03-14T04:33:23.6159593Z             device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2025-03-14T04:33:23.6160001Z                 the current :ref:`accelerator<accelerators>` device type. If not given,
2025-03-14T04:33:23.6160365Z                 use :func:`torch.accelerator.current_device_index` by default.
2025-03-14T04:33:23.6160517Z     
2025-03-14T04:33:23.6161093Z         .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2025-03-14T04:33:23.6161242Z     
2025-03-14T04:33:23.6161425Z         Example::
2025-03-14T04:33:23.6161574Z     
2025-03-14T04:33:23.6161838Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:23.6162323Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:23.6162592Z             >>> start_event = torch.Event(enable_timing=True)
2025-03-14T04:33:23.6162832Z             >>> end_event = torch.Event(enable_timing=True)
2025-03-14T04:33:23.6163080Z             >>> start_event.record()
2025-03-14T04:33:23.6163514Z             >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2025-03-14T04:33:23.6163713Z             >>> sum = torch.sum(tensor)
2025-03-14T04:33:23.6163894Z             >>> end_event.record()
2025-03-14T04:33:23.6164133Z             >>> torch.accelerator.synchronize()
2025-03-14T04:33:23.6164434Z             >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2025-03-14T04:33:23.6164601Z         
2025-03-14T04:33:23.6165627Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2025-03-14T04:33:23.6165798Z     
2025-03-14T04:33:23.6166273Z     assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-14T04:33:23.6166467Z                                             ^
2025-03-14T04:33:23.6166644Z       warnings.warn(msg)
2025-03-14T04:33:23.6166815Z     
2025-03-14T04:33:23.6167150Z --- Parse Warning: 15 / 116 ---
2025-03-14T04:33:23.6168710Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=396.
2025-03-14T04:33:23.6169162Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:23.6169395Z     Retrieves the CUDA runtime API module.
2025-03-14T04:33:23.6169583Z     
2025-03-14T04:33:23.6169782Z     
2025-03-14T04:33:23.6170238Z         This function initializes the CUDA runtime environment if it is not already
2025-03-14T04:33:23.6170675Z         initialized and returns the CUDA runtime API module (_cudart). The CUDA
2025-03-14T04:33:23.6171080Z         runtime API module provides access to various CUDA runtime functions.
2025-03-14T04:33:23.6171246Z     
2025-03-14T04:33:23.6171401Z         Args:
2025-03-14T04:33:23.6171582Z             ``None``
2025-03-14T04:33:23.6171735Z     
2025-03-14T04:33:23.6171890Z         Returns:
2025-03-14T04:33:23.6172163Z             module: The CUDA runtime API module (_cudart).
2025-03-14T04:33:23.6172312Z     
2025-03-14T04:33:23.6172482Z         Raises:
2025-03-14T04:33:23.6172896Z             RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2025-03-14T04:33:23.6173574Z             AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2025-03-14T04:33:23.6173728Z     
2025-03-14T04:33:23.6173980Z         Example of CUDA operations with profiling:
2025-03-14T04:33:23.6174151Z             >>> import torch
2025-03-14T04:33:23.6174444Z             >>> from torch.cuda import cudart, check_error
2025-03-14T04:33:23.6174610Z             >>> import os
2025-03-14T04:33:23.6174782Z             >>>
2025-03-14T04:33:23.6174989Z             >>> os.environ['CUDA_PROFILE'] = '1'
2025-03-14T04:33:23.6175161Z             >>>
2025-03-14T04:33:23.6175408Z             >>> def perform_cuda_operations_with_streams():
2025-03-14T04:33:23.6175635Z             >>>     stream = torch.cuda.Stream()
2025-03-14T04:33:23.6175846Z             >>>     with torch.cuda.stream(stream):
2025-03-14T04:33:23.6176091Z             >>>         x = torch.randn(100, 100, device='cuda')
2025-03-14T04:33:23.6176318Z             >>>         y = torch.randn(100, 100, device='cuda')
2025-03-14T04:33:23.6176525Z             >>>         z = torch.mul(x, y)
2025-03-14T04:33:23.6176692Z             >>>     return z
2025-03-14T04:33:23.6176868Z             >>>
2025-03-14T04:33:23.6177065Z             >>> torch.cuda.synchronize()
2025-03-14T04:33:23.6177322Z             >>> print("====== Start nsys profiling ======")
2025-03-14T04:33:23.6177561Z             >>> check_error(cudart().cudaProfilerStart())
2025-03-14T04:33:23.6177827Z             >>> with torch.autograd.profiler.emit_nvtx():
2025-03-14T04:33:23.6178104Z             >>>     result = perform_cuda_operations_with_streams()
2025-03-14T04:33:23.6178382Z             >>>     print("CUDA operations completed.")
2025-03-14T04:33:23.6178671Z             >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2025-03-14T04:33:23.6178917Z             >>> print("====== End nsys profiling ======")
2025-03-14T04:33:23.6179069Z     
2025-03-14T04:33:23.6179440Z         To run this example and save the profiling information, execute:
2025-03-14T04:33:23.6180094Z             >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-14T04:33:23.6180271Z     
2025-03-14T04:33:23.6180709Z         This command profiles the CUDA operations in the provided script and saves
2025-03-14T04:33:23.6181077Z         the profiling information to a file named `trace_name.prof`.
2025-03-14T04:33:23.6181498Z         The `--profile-from-start off` option ensures that profiling starts only
2025-03-14T04:33:23.6181785Z         after the `cudaProfilerStart` call in the script.
2025-03-14T04:33:23.6182183Z         The `--csv` and `--print-summary` options format the profiling output as a
2025-03-14T04:33:23.6182437Z         CSV file and print a summary, respectively.
2025-03-14T04:33:23.6182877Z         The `-o` option specifies the output file name, and the `-f` option forces the
2025-03-14T04:33:23.6183170Z         overwrite of the output file if it already exists.
2025-03-14T04:33:23.6183320Z         
2025-03-14T04:33:23.6184563Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2025-03-14T04:33:23.6184750Z     
2025-03-14T04:33:23.6185404Z     $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-14T04:33:23.6185556Z     ^
2025-03-14T04:33:23.6185748Z       warnings.warn(msg)
2025-03-14T04:33:23.6185894Z     
2025-03-14T04:33:23.6186242Z --- Parse Warning: 16 / 116 ---
2025-03-14T04:33:23.6187848Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=105.
2025-03-14T04:33:23.6188335Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6188482Z     
2025-03-14T04:33:23.6188908Z     Append the given callback function to this ``Future``, which will be run
2025-03-14T04:33:23.6189278Z     when the ``Future`` is completed.  Multiple callbacks can be added to
2025-03-14T04:33:23.6189668Z     the same ``Future``, but the order in which they will be executed cannot
2025-03-14T04:33:23.6190048Z     be guaranteed (to enforce a certain order consider chaining:
2025-03-14T04:33:23.6190418Z     ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2025-03-14T04:33:23.6190794Z     is the reference to this ``Future``. The callback function can use the
2025-03-14T04:33:23.6191174Z     :meth:`value` method to get the value. Note that if this ``Future`` is
2025-03-14T04:33:23.6191577Z     already completed, the given callback will be run immediately inline.
2025-03-14T04:33:23.6191742Z     
2025-03-14T04:33:23.6192086Z     If the ``Future``'s value contains tensors that reside on GPUs, the
2025-03-14T04:33:23.6192489Z     callback might be invoked while the async kernels that are populating
2025-03-14T04:33:23.6192901Z     those tensors haven't yet finished executing on the device. However, the
2025-03-14T04:33:23.6193297Z     callback will be invoked with some dedicated streams set as current
2025-03-14T04:33:23.6193659Z     (fetched from a global pool) which will be synchronized with those
2025-03-14T04:33:23.6194089Z     kernels. Hence any operation performed by the callback on these tensors
2025-03-14T04:33:23.6194465Z     will be scheduled on the device after the kernels complete. In other
2025-03-14T04:33:23.6194840Z     words, as long as the callback doesn't switch streams, it can safely
2025-03-14T04:33:23.6195286Z     manipulate the result without any additional synchronization. This is
2025-03-14T04:33:23.6195592Z     similar to the non-blocking behavior of :meth:`wait`.
2025-03-14T04:33:23.6195744Z     
2025-03-14T04:33:23.6196145Z     Similarly, if the callback returns a value that contains tensors that
2025-03-14T04:33:23.6196492Z     reside on a GPU, it can do so even if the kernels that are producing
2025-03-14T04:33:23.6196898Z     these tensors are still running on the device, as long as the callback
2025-03-14T04:33:23.6197269Z     didn't change streams during its execution. If one wants to change
2025-03-14T04:33:23.6197663Z     streams, one must be careful to re-synchronize them with the original
2025-03-14T04:33:23.6198054Z     streams, that is, those that were current when the callback was invoked.
2025-03-14T04:33:23.6198217Z     
2025-03-14T04:33:23.6198374Z     Args:
2025-03-14T04:33:23.6198754Z         callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2025-03-14T04:33:23.6198952Z                                 the only argument.
2025-03-14T04:33:23.6199180Z     
2025-03-14T04:33:23.6199340Z     Returns:
2025-03-14T04:33:23.6199640Z         A new ``Future`` object that holds the return value of the
2025-03-14T04:33:23.6199973Z         ``callback`` and will be marked as completed when the given
2025-03-14T04:33:23.6200152Z         ``callback`` finishes.
2025-03-14T04:33:23.6200322Z     
2025-03-14T04:33:23.6200634Z     .. note:: Note that if the callback function throws, either
2025-03-14T04:33:23.6201104Z         through the original future being completed with an exception and
2025-03-14T04:33:23.6201451Z         calling ``fut.wait()``, or through other code in the callback, the
2025-03-14T04:33:23.6201838Z         future returned by ``then`` will be marked appropriately with the
2025-03-14T04:33:23.6202191Z         encountered error. However, if this callback later completes
2025-03-14T04:33:23.6202607Z         additional futures, those futures are not marked as completed with
2025-03-14T04:33:23.6202970Z         an error and the user is responsible for handling completion/waiting
2025-03-14T04:33:23.6203192Z         on those futures independently.
2025-03-14T04:33:23.6203346Z     
2025-03-14T04:33:23.6203528Z     Example::
2025-03-14T04:33:23.6203790Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-14T04:33:23.6203984Z         >>> def callback(fut):
2025-03-14T04:33:23.6204228Z         ...     print(f"RPC return value is {fut.wait()}.")
2025-03-14T04:33:23.6204456Z         >>> fut = torch.futures.Future()
2025-03-14T04:33:23.6204759Z         >>> # The inserted callback will print the return value when
2025-03-14T04:33:23.6205024Z         >>> # receiving the response from "worker1"
2025-03-14T04:33:23.6205209Z         >>> cb_fut = fut.then(callback)
2025-03-14T04:33:23.6205420Z         >>> chain_cb_fut = cb_fut.then(
2025-03-14T04:33:23.6205687Z         ...     lambda x : print(f"Chained cb done. {x.wait()}")
2025-03-14T04:33:23.6205860Z         ... )
2025-03-14T04:33:23.6206039Z         >>> fut.set_result(5)
2025-03-14T04:33:23.6206237Z         RPC return value is 5.
2025-03-14T04:33:23.6206418Z         Chained cb done. None
2025-03-14T04:33:23.6206588Z     
2025-03-14T04:33:23.6207050Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6207212Z     
2025-03-14T04:33:23.6207384Z       warnings.warn(msg)
2025-03-14T04:33:23.6207553Z     
2025-03-14T04:33:23.6207895Z --- Parse Warning: 17 / 116 ---
2025-03-14T04:33:23.6209561Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=213.
2025-03-14T04:33:23.6210038Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6210206Z     
2025-03-14T04:33:23.6210612Z     Set the result for this ``Future``, which will mark this ``Future`` as
2025-03-14T04:33:23.6211025Z     completed and trigger all attached callbacks. Note that a ``Future``
2025-03-14T04:33:23.6211238Z     cannot be marked completed twice.
2025-03-14T04:33:23.6211408Z     
2025-03-14T04:33:23.6211800Z     If the result contains tensors that reside on GPUs, this method can be
2025-03-14T04:33:23.6212182Z     called even if the asynchronous kernels that are populating those
2025-03-14T04:33:23.6212572Z     tensors haven't yet completed running on the device, provided that the
2025-03-14T04:33:23.6212996Z     streams on which those kernels were enqueued are set as the current ones
2025-03-14T04:33:23.6213373Z     when this method is called. Put simply, it's safe to call this method
2025-03-14T04:33:23.6213773Z     immediately after launching those kernels, without any additional
2025-03-14T04:33:23.6214177Z     synchronization, as long as one doesn't change streams in between. This
2025-03-14T04:33:23.6214595Z     method will record events on all the relevant current streams and will
2025-03-14T04:33:23.6214951Z     use them to ensure proper scheduling for all the consumers of this
2025-03-14T04:33:23.6215128Z     ``Future``.
2025-03-14T04:33:23.6215279Z     
2025-03-14T04:33:23.6215441Z     Args:
2025-03-14T04:33:23.6215726Z         result (object): the result object of this ``Future``.
2025-03-14T04:33:23.6215894Z     
2025-03-14T04:33:23.6216054Z     Example::
2025-03-14T04:33:23.6216316Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-14T04:33:23.6216572Z         >>> import threading
2025-03-14T04:33:23.6216734Z         >>> import time
2025-03-14T04:33:23.6216951Z         >>> def slow_set_future(fut, value):
2025-03-14T04:33:23.6217128Z         ...     time.sleep(0.5)
2025-03-14T04:33:23.6217330Z         ...     fut.set_result(value)
2025-03-14T04:33:23.6217535Z         >>> fut = torch.futures.Future()
2025-03-14T04:33:23.6217727Z         >>> t = threading.Thread(
2025-03-14T04:33:23.6217912Z         ...     target=slow_set_future,
2025-03-14T04:33:23.6218136Z         ...     args=(fut, torch.ones(2) * 3)
2025-03-14T04:33:23.6218290Z         ... )
2025-03-14T04:33:23.6218461Z         >>> t.start()
2025-03-14T04:33:23.6218637Z         >>> print(fut.wait())
2025-03-14T04:33:23.6218819Z         tensor([3., 3.])
2025-03-14T04:33:23.6218973Z         >>> t.join()
2025-03-14T04:33:23.6219138Z     
2025-03-14T04:33:23.6219597Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6219761Z     
2025-03-14T04:33:23.6219941Z       warnings.warn(msg)
2025-03-14T04:33:23.6220105Z     
2025-03-14T04:33:23.6220437Z --- Parse Warning: 18 / 116 ---
2025-03-14T04:33:23.6222095Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144.
2025-03-14T04:33:23.6222573Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6222993Z     Compiles compute shader from source and allows one to invoke kernels
2025-03-14T04:33:23.6223262Z         defined there from the comfort of Python runtime
2025-03-14T04:33:23.6223438Z         Example::
2025-03-14T04:33:23.6223585Z     
2025-03-14T04:33:23.6223841Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS)
2025-03-14T04:33:23.6224053Z             >>> lib = torch.mps.compile_shader(
2025-03-14T04:33:23.6224767Z             ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }"
2025-03-14T04:33:23.6224925Z             ...  )
2025-03-14T04:33:23.6225148Z             >>> x = torch.zeros(16, device="mps")
2025-03-14T04:33:23.6225328Z             >>> lib.full(x, 3.14)
2025-03-14T04:33:23.6225497Z         
2025-03-14T04:33:23.6225954Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6226118Z     
2025-03-14T04:33:23.6226295Z       warnings.warn(msg)
2025-03-14T04:33:23.6226492Z     
2025-03-14T04:33:23.6226816Z --- Parse Warning: 19 / 116 ---
2025-03-14T04:33:23.6228349Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202.
2025-03-14T04:33:23.6228822Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6229127Z     Return the sum of each row of the given sparse tensor.
2025-03-14T04:33:23.6229282Z     
2025-03-14T04:33:23.6229703Z         Returns the sum of each row of the sparse tensor :attr:`input` in the given
2025-03-14T04:33:23.6230053Z         dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2025-03-14T04:33:23.6230441Z         reduce over all of them. When sum over all ``sparse_dim``, this method
2025-03-14T04:33:23.6230715Z         returns a dense tensor instead of a sparse tensor.
2025-03-14T04:33:23.6230884Z     
2025-03-14T04:33:23.6231344Z         All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2025-03-14T04:33:23.6231703Z         tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2025-03-14T04:33:23.6231851Z     
2025-03-14T04:33:23.6232246Z         During backward, only gradients at ``nnz`` locations of :attr:`input`
2025-03-14T04:33:23.6232696Z         will propagate back. Note that the gradients of :attr:`input` is coalesced.
2025-03-14T04:33:23.6232849Z     
2025-03-14T04:33:23.6233881Z         Args:
2025-03-14T04:33:23.6234110Z             input (Tensor): the input sparse tensor
2025-03-14T04:33:23.6234623Z             dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2025-03-14T04:33:23.6234802Z                 over all dims.
2025-03-14T04:33:23.6235281Z             dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2025-03-14T04:33:23.6235490Z                 Default: dtype of :attr:`input`.
2025-03-14T04:33:23.6235657Z     
2025-03-14T04:33:23.6235821Z         Example::
2025-03-14T04:33:23.6235994Z     
2025-03-14T04:33:23.6236152Z             >>> nnz = 3
2025-03-14T04:33:23.6236340Z             >>> dims = [5, 5, 2, 3]
2025-03-14T04:33:23.6236621Z             >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2025-03-14T04:33:23.6236972Z                                torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2025-03-14T04:33:23.6237196Z             >>> V = torch.randn(nnz, dims[2], dims[3])
2025-03-14T04:33:23.6237393Z             >>> size = torch.Size(dims)
2025-03-14T04:33:23.6237642Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:23.6237927Z             >>> S = torch.sparse_coo_tensor(I, V, size)
2025-03-14T04:33:23.6238082Z             >>> S
2025-03-14T04:33:23.6238305Z             tensor(indices=tensor([[2, 0, 3],
2025-03-14T04:33:23.6238484Z                                    [2, 4, 1]]),
2025-03-14T04:33:23.6238732Z                    values=tensor([[[-0.6438, -1.6467,  1.4004],
2025-03-14T04:33:23.6238933Z                                    [ 0.3411,  0.0918, -0.2312]],
2025-03-14T04:33:23.6239097Z     
2025-03-14T04:33:23.6239360Z                                   [[ 0.5348,  0.0634, -2.0494],
2025-03-14T04:33:23.6239577Z                                    [-0.7125, -1.0646,  2.1844]],
2025-03-14T04:33:23.6239727Z     
2025-03-14T04:33:23.6239940Z                                   [[ 0.1276,  0.1874, -0.6334],
2025-03-14T04:33:23.6240143Z                                    [-1.9682, -0.5340,  0.7483]]]),
2025-03-14T04:33:23.6240412Z                    size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2025-03-14T04:33:23.6240562Z     
2025-03-14T04:33:23.6240922Z             # when sum over only part of sparse_dims, return a sparse tensor
2025-03-14T04:33:23.6241121Z             >>> torch.sparse.sum(S, [1, 3])
2025-03-14T04:33:23.6241336Z             tensor(indices=tensor([[0, 2, 3]]),
2025-03-14T04:33:23.6241585Z                    values=tensor([[-1.4512,  0.4073],
2025-03-14T04:33:23.6241778Z                                   [-0.8901,  0.2017],
2025-03-14T04:33:23.6241965Z                                   [-0.3183, -1.7539]]),
2025-03-14T04:33:23.6242217Z                    size=(5, 2), nnz=3, layout=torch.sparse_coo)
2025-03-14T04:33:23.6242365Z     
2025-03-14T04:33:23.6242656Z             # when sum over all sparse dim, return a dense tensor
2025-03-14T04:33:23.6242847Z             # with summed dims squeezed
2025-03-14T04:33:23.6243069Z             >>> torch.sparse.sum(S, [0, 1, 3])
2025-03-14T04:33:23.6243251Z             tensor([-2.6596, -1.1450])
2025-03-14T04:33:23.6243416Z         
2025-03-14T04:33:23.6243877Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6244046Z     
2025-03-14T04:33:23.6244220Z       warnings.warn(msg)
2025-03-14T04:33:23.6244368Z     
2025-03-14T04:33:23.6244728Z --- Parse Warning: 20 / 116 ---
2025-03-14T04:33:23.6246265Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39.
2025-03-14T04:33:23.6246750Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6246903Z     
2025-03-14T04:33:23.6247310Z     vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2025-03-14T04:33:23.6247699Z     maps ``func`` over some dimension of the inputs. Semantically, vmap
2025-03-14T04:33:23.6248166Z     pushes the map into PyTorch operations called by ``func``, effectively
2025-03-14T04:33:23.6248367Z     vectorizing those operations.
2025-03-14T04:33:23.6248533Z     
2025-03-14T04:33:23.6249059Z     vmap is useful for handling batch dimensions: one can write a function
2025-03-14T04:33:23.6249438Z     ``func`` that runs on examples and then lift it to a function that can
2025-03-14T04:33:23.6249826Z     take batches of examples with ``vmap(func)``. vmap can also be used to
2025-03-14T04:33:23.6250151Z     compute batched gradients when composed with autograd.
2025-03-14T04:33:23.6250300Z     
2025-03-14T04:33:23.6250476Z     .. note::
2025-03-14T04:33:23.6250801Z         :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2025-03-14T04:33:23.6251047Z         convenience. Use whichever one you'd like.
2025-03-14T04:33:23.6251198Z     
2025-03-14T04:33:23.6251377Z     Args:
2025-03-14T04:33:23.6251757Z         func (function): A Python function that takes one or more arguments.
2025-03-14T04:33:23.6251982Z             Must return one or more Tensors.
2025-03-14T04:33:23.6252419Z         in_dims (int or nested structure): Specifies which dimension of the
2025-03-14T04:33:23.6252743Z             inputs should be mapped over. ``in_dims`` should have a
2025-03-14T04:33:23.6253087Z             structure like the inputs. If the ``in_dim`` for a particular
2025-03-14T04:33:23.6253436Z             input is None, then that indicates there is no map dimension.
2025-03-14T04:33:23.6253600Z             Default: 0.
2025-03-14T04:33:23.6253967Z         out_dims (int or Tuple[int]): Specifies where the mapped dimension
2025-03-14T04:33:23.6254312Z             should appear in the outputs. If ``out_dims`` is a Tuple, then
2025-03-14T04:33:23.6254606Z             it should have one element per output. Default: 0.
2025-03-14T04:33:23.6254940Z         randomness (str): Specifies whether the randomness in this
2025-03-14T04:33:23.6255339Z             vmap should be the same or different across batches. If 'different',
2025-03-14T04:33:23.6255701Z             the randomness for each batch will be different. If 'same', the
2025-03-14T04:33:23.6256110Z             randomness will be the same across batches. If 'error', any calls to
2025-03-14T04:33:23.6256487Z             random functions will error. Default: 'error'. WARNING: this flag
2025-03-14T04:33:23.6256869Z             only applies to random PyTorch operations and does not apply to
2025-03-14T04:33:23.6257157Z             Python's random module or numpy randomness.
2025-03-14T04:33:23.6257584Z         chunk_size (None or int): If None (default), apply a single vmap over inputs.
2025-03-14T04:33:23.6257980Z             If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2025-03-14T04:33:23.6258459Z             Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2025-03-14T04:33:23.6258945Z             If you run into memory issues computing the vmap, please try a non-None chunk_size.
2025-03-14T04:33:23.6259112Z     
2025-03-14T04:33:23.6259273Z     Returns:
2025-03-14T04:33:23.6259627Z         Returns a new "batched" function. It takes the same inputs as
2025-03-14T04:33:23.6259957Z         ``func``, except each input has an extra dimension at the index
2025-03-14T04:33:23.6260307Z         specified by ``in_dims``. It takes returns the same outputs as
2025-03-14T04:33:23.6260643Z         ``func``, except each output has an extra dimension at the index
2025-03-14T04:33:23.6260841Z         specified by ``out_dims``.
2025-03-14T04:33:23.6260993Z     
2025-03-14T04:33:23.6261167Z     .. warning:
2025-03-14T04:33:23.6261525Z         :func:`vmap` works best with functional-style code. Please do not
2025-03-14T04:33:23.6261869Z         perform any side-effects in ``func``, with the exception of
2025-03-14T04:33:23.6262293Z         in-place PyTorch operations. Examples of side-effects include mutating
2025-03-14T04:33:23.6262760Z         Python data structures and assigning values to variables not captured
2025-03-14T04:33:23.6262967Z         in ``func``.
2025-03-14T04:33:23.6263136Z     
2025-03-14T04:33:23.6263563Z     One example of using :func:`vmap` is to compute batched dot products. PyTorch
2025-03-14T04:33:23.6263962Z     doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2025-03-14T04:33:23.6264353Z     rummaging through docs, use :func:`vmap` to construct a new function.
2025-03-14T04:33:23.6264524Z     
2025-03-14T04:33:23.6264769Z         >>> torch.dot                            # [D], [D] -> []
2025-03-14T04:33:23.6265143Z         >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2025-03-14T04:33:23.6265369Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-14T04:33:23.6265565Z         >>> batched_dot(x, y)
2025-03-14T04:33:23.6265715Z     
2025-03-14T04:33:23.6266132Z     :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2025-03-14T04:33:23.6266328Z     model authoring experience.
2025-03-14T04:33:23.6266497Z     
2025-03-14T04:33:23.6266693Z         >>> batch_size, feature_size = 3, 5
2025-03-14T04:33:23.6267045Z         >>> weights = torch.randn(feature_size, requires_grad=True)
2025-03-14T04:33:23.6267200Z         >>>
2025-03-14T04:33:23.6267400Z         >>> def model(feature_vec):
2025-03-14T04:33:23.6267634Z         >>>     # Very simple linear model with activation
2025-03-14T04:33:23.6267873Z         >>>     return feature_vec.dot(weights).relu()
2025-03-14T04:33:23.6268031Z         >>>
2025-03-14T04:33:23.6268296Z         >>> examples = torch.randn(batch_size, feature_size)
2025-03-14T04:33:23.6268519Z         >>> result = torch.vmap(model)(examples)
2025-03-14T04:33:23.6268668Z     
2025-03-14T04:33:23.6269132Z     :func:`vmap` can also help vectorize computations that were previously difficult
2025-03-14T04:33:23.6269543Z     or impossible to batch. One example is higher-order gradient computation.
2025-03-14T04:33:23.6269973Z     The PyTorch autograd engine computes vjps (vector-Jacobian products).
2025-03-14T04:33:23.6270387Z     Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2025-03-14T04:33:23.6270849Z     requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2025-03-14T04:33:23.6271271Z     we can vectorize the whole computation, computing the Jacobian in a single
2025-03-14T04:33:23.6271472Z     call to ``autograd.grad``.
2025-03-14T04:33:23.6271616Z     
2025-03-14T04:33:23.6271831Z         >>> # Setup
2025-03-14T04:33:23.6271986Z         >>> N = 5
2025-03-14T04:33:23.6272178Z         >>> f = lambda x: x ** 2
2025-03-14T04:33:23.6272392Z         >>> x = torch.randn(N, requires_grad=True)
2025-03-14T04:33:23.6272566Z         >>> y = f(x)
2025-03-14T04:33:23.6272740Z         >>> I_N = torch.eye(N)
2025-03-14T04:33:23.6272911Z         >>>
2025-03-14T04:33:23.6273098Z         >>> # Sequential approach
2025-03-14T04:33:23.6273498Z         >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2025-03-14T04:33:23.6273701Z         >>>                  for v in I_N.unbind()]
2025-03-14T04:33:23.6273931Z         >>> jacobian = torch.stack(jacobian_rows)
2025-03-14T04:33:23.6274087Z         >>>
2025-03-14T04:33:23.6274312Z         >>> # vectorized gradient computation
2025-03-14T04:33:23.6274481Z         >>> def get_vjp(v):
2025-03-14T04:33:23.6274714Z         >>>     return torch.autograd.grad(y, x, v)
2025-03-14T04:33:23.6274928Z         >>> jacobian = torch.vmap(get_vjp)(I_N)
2025-03-14T04:33:23.6275100Z     
2025-03-14T04:33:23.6275567Z     :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2025-03-14T04:33:23.6275729Z     
2025-03-14T04:33:23.6275978Z         >>> torch.dot                            # [D], [D] -> []
2025-03-14T04:33:23.6276493Z         >>> batched_dot = torch.vmap(torch.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2025-03-14T04:33:23.6276735Z         >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2025-03-14T04:33:23.6277036Z         >>> batched_dot(x, y) # tensor of size [2, 3]
2025-03-14T04:33:23.6277188Z     
2025-03-14T04:33:23.6277633Z     If the inputs are not batched along the first dimension, ``in_dims`` specifies
2025-03-14T04:33:23.6277903Z     the dimension that each inputs are batched along as
2025-03-14T04:33:23.6278076Z     
2025-03-14T04:33:23.6278324Z         >>> torch.dot                            # [N], [N] -> []
2025-03-14T04:33:23.6278736Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2025-03-14T04:33:23.6278960Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-14T04:33:23.6279494Z         >>> batched_dot(x, y)   # output is [5] instead of [2] if batched along the 0th dimension
2025-03-14T04:33:23.6279645Z     
2025-03-14T04:33:23.6280122Z     If there are multiple inputs each of which is batched along different dimensions,
2025-03-14T04:33:23.6280471Z     ``in_dims`` must be a tuple with the batch dimension for each input as
2025-03-14T04:33:23.6280641Z     
2025-03-14T04:33:23.6280893Z         >>> torch.dot                            # [D], [D] -> []
2025-03-14T04:33:23.6281325Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2025-03-14T04:33:23.6281580Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-14T04:33:23.6282031Z         >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None
2025-03-14T04:33:23.6282188Z     
2025-03-14T04:33:23.6282636Z     If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2025-03-14T04:33:23.6282836Z     matching the shape of the input:
2025-03-14T04:33:23.6282991Z     
2025-03-14T04:33:23.6283261Z         >>> f = lambda dict: torch.dot(dict['x'], dict['y'])
2025-03-14T04:33:23.6283469Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-14T04:33:23.6283667Z         >>> input = {'x': x, 'y': y}
2025-03-14T04:33:23.6283986Z         >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
2025-03-14T04:33:23.6284197Z         >>> batched_dot(input)
2025-03-14T04:33:23.6284349Z     
2025-03-14T04:33:23.6284857Z     By default, the output is batched along the first dimension. However, it can be batched
2025-03-14T04:33:23.6285085Z     along any dimension by using ``out_dims``
2025-03-14T04:33:23.6285252Z     
2025-03-14T04:33:23.6285430Z         >>> f = lambda x: x ** 2
2025-03-14T04:33:23.6285630Z         >>> x = torch.randn(2, 5)
2025-03-14T04:33:23.6285881Z         >>> batched_pow = torch.vmap(f, out_dims=1)
2025-03-14T04:33:23.6286084Z         >>> batched_pow(x) # [5, 2]
2025-03-14T04:33:23.6286238Z     
2025-03-14T04:33:23.6286782Z     For any function that uses kwargs, the returned function will not batch the kwargs but will
2025-03-14T04:33:23.6286952Z     accept kwargs
2025-03-14T04:33:23.6287119Z     
2025-03-14T04:33:23.6287299Z         >>> x = torch.randn([2, 5])
2025-03-14T04:33:23.6287495Z         >>> def fn(x, scale=4.):
2025-03-14T04:33:23.6287666Z         >>>   return x * scale
2025-03-14T04:33:23.6287836Z         >>>
2025-03-14T04:33:23.6288032Z         >>> batched_pow = torch.vmap(fn)
2025-03-14T04:33:23.6288290Z         >>> assert torch.allclose(batched_pow(x), x * 4)
2025-03-14T04:33:23.6288691Z         >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]
2025-03-14T04:33:23.6288852Z     
2025-03-14T04:33:23.6289008Z     .. note::
2025-03-14T04:33:23.6289417Z         vmap does not provide general autobatching or handle variable-length
2025-03-14T04:33:23.6289604Z         sequences out of the box.
2025-03-14T04:33:23.6289770Z     
2025-03-14T04:33:23.6290222Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6290391Z     
2025-03-14T04:33:23.6290564Z       warnings.warn(msg)
2025-03-14T04:33:23.6290712Z     
2025-03-14T04:33:23.6291092Z --- Parse Warning: 21 / 116 ---
2025-03-14T04:33:23.6292703Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=21.
2025-03-14T04:33:23.6293233Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6293709Z     Create a custom operator whose implementation is backed by 1+ triton kernels.
2025-03-14T04:33:23.6293856Z     
2025-03-14T04:33:23.6294236Z         This is a more structured way of using triton kernels with PyTorch.
2025-03-14T04:33:23.6294693Z         Prefer using triton kernels with no ``torch.library`` custom operator wrappers
2025-03-14T04:33:23.6295151Z         (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because
2025-03-14T04:33:23.6295324Z         that is simpler;
2025-03-14T04:33:23.6295797Z         only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you
2025-03-14T04:33:23.6296207Z         want to create an operator that behaves like PyTorch built-in operators.
2025-03-14T04:33:23.6296603Z         For example, you may use a ``torch.library`` wrapper API to define the
2025-03-14T04:33:23.6296991Z         behavior of the triton kernel when passed a tensor subclass or under
2025-03-14T04:33:23.6297230Z         a TorchDispatchMode.
2025-03-14T04:33:23.6297376Z     
2025-03-14T04:33:23.6297830Z         Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op`
2025-03-14T04:33:23.6298032Z         when the implementation
2025-03-14T04:33:23.6298420Z         consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2025-03-14T04:33:23.6298739Z         custom operators as opaque (:func:`torch.compile` and
2025-03-14T04:33:23.6299158Z         :func:`torch.export.export` will never trace into them), but ``triton_op``
2025-03-14T04:33:23.6299568Z         makes the implementation visible to these subsystems, allowing them
2025-03-14T04:33:23.6299772Z         to optimize the triton kernel(s).
2025-03-14T04:33:23.6299939Z     
2025-03-14T04:33:23.6300281Z         Note that ``fn`` must only consist of calls to PyTorch-understood
2025-03-14T04:33:23.6300710Z         operators and triton kernels. Any triton kernels called inside ``fn``
2025-03-14T04:33:23.6301052Z         must be wrapped in a call to :func:`torch.library.wrap_triton`.
2025-03-14T04:33:23.6301222Z     
2025-03-14T04:33:23.6301374Z         Args:
2025-03-14T04:33:23.6301790Z             name (str): A name for the custom op that looks like "{namespace}::{name}",
2025-03-14T04:33:23.6302200Z                 e.g. "mylib::my_linear". The name is used as the op's stable identifier
2025-03-14T04:33:23.6302508Z                 in PyTorch subsystems (e.g. torch.export, FX graphs).
2025-03-14T04:33:23.6302933Z                 To avoid name collisions, please use your project name as the namespace;
2025-03-14T04:33:23.6303318Z                 e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2025-03-14T04:33:23.6303810Z             mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2025-03-14T04:33:23.6304266Z                 This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2025-03-14T04:33:23.6304761Z                 it pessimistically assumes that all inputs to the operator are being mutated.
2025-03-14T04:33:23.6305094Z             schema (None | str): A schema string for the operator. If None
2025-03-14T04:33:23.6305482Z                 (recommended) we'll infer a schema for the operator from its type
2025-03-14T04:33:23.6305852Z                 annotations. We recommend letting us infer a schema unless you
2025-03-14T04:33:23.6306079Z                 have a specific reason not to.
2025-03-14T04:33:23.6306346Z                 Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2025-03-14T04:33:23.6306512Z     
2025-03-14T04:33:23.6306669Z         Example::
2025-03-14T04:33:23.6306821Z     
2025-03-14T04:33:23.6307081Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:23.6307257Z             >>> import torch
2025-03-14T04:33:23.6307633Z             >>> from torch.library import triton_op, wrap_triton
2025-03-14T04:33:23.6307790Z             >>>
2025-03-14T04:33:23.6307983Z             >>> import triton
2025-03-14T04:33:23.6308194Z             >>> from triton import language as tl
2025-03-14T04:33:23.6308364Z             >>>
2025-03-14T04:33:23.6308531Z             >>> @triton.jit
2025-03-14T04:33:23.6308721Z             >>> def add_kernel(
2025-03-14T04:33:23.6308888Z             >>>     in_ptr0,
2025-03-14T04:33:23.6309074Z             >>>     in_ptr1,
2025-03-14T04:33:23.6309234Z             >>>     out_ptr,
2025-03-14T04:33:23.6309412Z             >>>     n_elements,
2025-03-14T04:33:23.6309620Z             >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-14T04:33:23.6309793Z             >>> ):
2025-03-14T04:33:23.6309996Z             >>>     pid = tl.program_id(axis=0)
2025-03-14T04:33:23.6310222Z             >>>     block_start = pid * BLOCK_SIZE
2025-03-14T04:33:23.6310494Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-14T04:33:23.6310701Z             >>>     mask = offsets < n_elements
2025-03-14T04:33:23.6310924Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-14T04:33:23.6311201Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-14T04:33:23.6311380Z             >>>     output = x + y
2025-03-14T04:33:23.6311655Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-14T04:33:23.6311816Z             >>>
2025-03-14T04:33:23.6312067Z             >>> @triton_op("mylib::add", mutates_args={})
2025-03-14T04:33:23.6312382Z             >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2025-03-14T04:33:23.6312599Z             >>>     output = torch.empty_like(x)
2025-03-14T04:33:23.6312805Z             >>>     n_elements = output.numel()
2025-03-14T04:33:23.6312974Z             >>>
2025-03-14T04:33:23.6313144Z             >>>     def grid(meta):
2025-03-14T04:33:23.6313457Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-14T04:33:23.6313614Z             >>>
2025-03-14T04:33:23.6313954Z             >>>     # NB: we need to wrap the triton kernel in a call to wrap_triton
2025-03-14T04:33:23.6314289Z             >>>     wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2025-03-14T04:33:23.6314476Z             >>>     return output
2025-03-14T04:33:23.6314629Z             >>>
2025-03-14T04:33:23.6314802Z             >>> @torch.compile
2025-03-14T04:33:23.6315015Z             >>> def f(x, y):
2025-03-14T04:33:23.6315190Z             >>>     return add(x, y)
2025-03-14T04:33:23.6315360Z             >>>
2025-03-14T04:33:23.6315564Z             >>> x = torch.randn(3, device="cuda")
2025-03-14T04:33:23.6315776Z             >>> y = torch.randn(3, device="cuda")
2025-03-14T04:33:23.6315932Z             >>>
2025-03-14T04:33:23.6316109Z             >>> z = f(x, y)
2025-03-14T04:33:23.6316315Z             >>> assert torch.allclose(z, x + y)
2025-03-14T04:33:23.6316481Z     
2025-03-14T04:33:23.6316632Z         
2025-03-14T04:33:23.6317108Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6317257Z     
2025-03-14T04:33:23.6317448Z       warnings.warn(msg)
2025-03-14T04:33:23.6317591Z     
2025-03-14T04:33:23.6317944Z --- Parse Warning: 22 / 116 ---
2025-03-14T04:33:23.6319621Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=202.
2025-03-14T04:33:23.6320110Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6320449Z     Allows capture of a triton kernel into a graph via make_fx or
2025-03-14T04:33:23.6320662Z         non-strict ``torch.export``.
2025-03-14T04:33:23.6320806Z     
2025-03-14T04:33:23.6321148Z         These technologies perform Dispatcher-based tracing (via
2025-03-14T04:33:23.6321549Z         ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2025-03-14T04:33:23.6321951Z         The ``wrap_triton`` API wraps a triton kernel into a callable that
2025-03-14T04:33:23.6322163Z         can actually be traced into a graph.
2025-03-14T04:33:23.6322328Z     
2025-03-14T04:33:23.6322701Z         Please use this API together with :func:`torch.library.triton_op`.
2025-03-14T04:33:23.6322869Z     
2025-03-14T04:33:23.6323031Z         Examples:
2025-03-14T04:33:23.6323203Z     
2025-03-14T04:33:23.6323384Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6323572Z             >>> import torch
2025-03-14T04:33:23.6323745Z             >>> import triton
2025-03-14T04:33:23.6323974Z             >>> from triton import language as tl
2025-03-14T04:33:23.6324299Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-14T04:33:23.6324542Z             >>> from torch.library import wrap_triton
2025-03-14T04:33:23.6324697Z             >>>
2025-03-14T04:33:23.6324882Z             >>> @triton.jit
2025-03-14T04:33:23.6325058Z             >>> def add_kernel(
2025-03-14T04:33:23.6325219Z             >>>     in_ptr0,
2025-03-14T04:33:23.6325400Z             >>>     in_ptr1,
2025-03-14T04:33:23.6325600Z             >>>     out_ptr,
2025-03-14T04:33:23.6325785Z             >>>     n_elements,
2025-03-14T04:33:23.6325991Z             >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-14T04:33:23.6326160Z             >>> ):
2025-03-14T04:33:23.6326361Z             >>>     pid = tl.program_id(axis=0)
2025-03-14T04:33:23.6326588Z             >>>     block_start = pid * BLOCK_SIZE
2025-03-14T04:33:23.6326855Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-14T04:33:23.6327070Z             >>>     mask = offsets < n_elements
2025-03-14T04:33:23.6327297Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-14T04:33:23.6327540Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-14T04:33:23.6327716Z             >>>     output = x + y
2025-03-14T04:33:23.6327996Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-14T04:33:23.6328148Z             >>>
2025-03-14T04:33:23.6328335Z             >>> def add(x, y):
2025-03-14T04:33:23.6328546Z             >>>     output = torch.empty_like(x)
2025-03-14T04:33:23.6328769Z             >>>     n_elements = output.numel()
2025-03-14T04:33:23.6328922Z             >>>
2025-03-14T04:33:23.6329124Z             >>>     def grid_fn(meta):
2025-03-14T04:33:23.6329464Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-14T04:33:23.6329635Z             >>>
2025-03-14T04:33:23.6329989Z             >>>     wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2025-03-14T04:33:23.6330184Z             >>>     return output
2025-03-14T04:33:23.6330340Z             >>>
2025-03-14T04:33:23.6330567Z             >>> x = torch.randn(3, device="cuda")
2025-03-14T04:33:23.6330768Z             >>> y = torch.randn(3, device="cuda")
2025-03-14T04:33:23.6330979Z             >>> gm = make_fx(add)(x, y)
2025-03-14T04:33:23.6331159Z             >>> print(gm.code)
2025-03-14T04:33:23.6331373Z             >>> # def forward(self, x_1, y_1):
2025-03-14T04:33:23.6331799Z             >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2025-03-14T04:33:23.6332256Z             >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2025-03-14T04:33:23.6332487Z             >>> #         kernel_idx = 0, constant_args_idx = 0,
2025-03-14T04:33:23.6332707Z             >>> #         grid = [(1, 1, 1)], kwargs = {
2025-03-14T04:33:23.6332984Z             >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2025-03-14T04:33:23.6333220Z             >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2025-03-14T04:33:23.6333383Z             >>> #         })
2025-03-14T04:33:23.6333592Z             >>> #     return empty_like
2025-03-14T04:33:23.6333744Z     
2025-03-14T04:33:23.6333911Z         
2025-03-14T04:33:23.6334404Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6334600Z     
2025-03-14T04:33:23.6334773Z       warnings.warn(msg)
2025-03-14T04:33:23.6334921Z     
2025-03-14T04:33:23.6335282Z --- Parse Warning: 23 / 116 ---
2025-03-14T04:33:23.6337005Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=326.
2025-03-14T04:33:23.6337494Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6337663Z     
2025-03-14T04:33:23.6338030Z     Raises an AssertionError if two items are not equal up to desired
2025-03-14T04:33:23.6338184Z     precision.
2025-03-14T04:33:23.6338353Z     
2025-03-14T04:33:23.6338656Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-03-14T04:33:23.6338996Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-14T04:33:23.6339331Z               instead of this function for more consistent floating point
2025-03-14T04:33:23.6339549Z               comparisons.
2025-03-14T04:33:23.6339696Z     
2025-03-14T04:33:23.6340094Z     The test verifies that the elements of `actual` and `desired` satisfy.
2025-03-14T04:33:23.6340237Z     
2025-03-14T04:33:23.6340531Z         ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2025-03-14T04:33:23.6340678Z     
2025-03-14T04:33:23.6341111Z     That is a looser test than originally documented, but agrees with what the
2025-03-14T04:33:23.6341530Z     actual implementation in `assert_array_almost_equal` did up to rounding
2025-03-14T04:33:23.6341975Z     vagaries. An exception is raised at conflicting values. For ndarrays this
2025-03-14T04:33:23.6342190Z     delegates to assert_array_almost_equal
2025-03-14T04:33:23.6342360Z     
2025-03-14T04:33:23.6342521Z     Parameters
2025-03-14T04:33:23.6342701Z     ----------
2025-03-14T04:33:23.6342877Z     actual : array_like
2025-03-14T04:33:23.6343069Z         The object to check.
2025-03-14T04:33:23.6343244Z     desired : array_like
2025-03-14T04:33:23.6343439Z         The expected object.
2025-03-14T04:33:23.6343619Z     decimal : int, optional
2025-03-14T04:33:23.6343839Z         Desired precision, default is 7.
2025-03-14T04:33:23.6344017Z     err_msg : str, optional
2025-03-14T04:33:23.6344311Z         The error message to be printed in case of failure.
2025-03-14T04:33:23.6344523Z     verbose : bool, optional
2025-03-14T04:33:23.6344907Z         If True, the conflicting values are appended to the error message.
2025-03-14T04:33:23.6345054Z     
2025-03-14T04:33:23.6345220Z     Raises
2025-03-14T04:33:23.6345376Z     ------
2025-03-14T04:33:23.6345558Z     AssertionError
2025-03-14T04:33:23.6345905Z       If actual and desired are not equal up to specified precision.
2025-03-14T04:33:23.6346054Z     
2025-03-14T04:33:23.6346224Z     See Also
2025-03-14T04:33:23.6346381Z     --------
2025-03-14T04:33:23.6346831Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:23.6347055Z                      relative and/or absolute precision.
2025-03-14T04:33:23.6347456Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:23.6347609Z     
2025-03-14T04:33:23.6347782Z     Examples
2025-03-14T04:33:23.6347938Z     --------
2025-03-14T04:33:23.6348244Z     >>> from torch._numpy.testing import assert_almost_equal
2025-03-14T04:33:23.6348486Z     >>> assert_almost_equal(2.3333333333333, 2.33333334)
2025-03-14T04:33:23.6348992Z     >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2025-03-14T04:33:23.6349199Z     Traceback (most recent call last):
2025-03-14T04:33:23.6349372Z         ...
2025-03-14T04:33:23.6349546Z     AssertionError:
2025-03-14T04:33:23.6349785Z     Arrays are not almost equal to 10 decimals
2025-03-14T04:33:23.6349957Z      ACTUAL: 2.3333333333333
2025-03-14T04:33:23.6350141Z      DESIRED: 2.33333334
2025-03-14T04:33:23.6350428Z     
2025-03-14T04:33:23.6350713Z     >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
2025-03-14T04:33:23.6350933Z     ...                     np.array([1.0,2.33333334]), decimal=9)
2025-03-14T04:33:23.6351162Z     Traceback (most recent call last):
2025-03-14T04:33:23.6351315Z         ...
2025-03-14T04:33:23.6351506Z     AssertionError:
2025-03-14T04:33:23.6351724Z     Arrays are not almost equal to 9 decimals
2025-03-14T04:33:23.6351896Z     <BLANKLINE>
2025-03-14T04:33:23.6352095Z     Mismatched elements: 1 / 2 (50%)
2025-03-14T04:33:23.6352344Z     Max absolute difference: 6.666699636781459e-09
2025-03-14T04:33:23.6352573Z     Max relative difference: 2.8571569790287484e-09
2025-03-14T04:33:23.6352837Z      x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-14T04:33:23.6353075Z      y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-14T04:33:23.6353227Z     
2025-03-14T04:33:23.6353395Z     
2025-03-14T04:33:23.6353853Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6354019Z     
2025-03-14T04:33:23.6354200Z       warnings.warn(msg)
2025-03-14T04:33:23.6354414Z     
2025-03-14T04:33:23.6354761Z --- Parse Warning: 24 / 116 ---
2025-03-14T04:33:23.6356496Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=451.
2025-03-14T04:33:23.6356968Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6357134Z     
2025-03-14T04:33:23.6357532Z     Raises an AssertionError if two items are not equal up to significant
2025-03-14T04:33:23.6357709Z     digits.
2025-03-14T04:33:23.6357860Z     
2025-03-14T04:33:23.6358177Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-03-14T04:33:23.6358499Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-14T04:33:23.6358846Z               instead of this function for more consistent floating point
2025-03-14T04:33:23.6359014Z               comparisons.
2025-03-14T04:33:23.6359240Z     
2025-03-14T04:33:23.6359571Z     Given two numbers, check that they are approximately equal.
2025-03-14T04:33:23.6359978Z     Approximately equal is defined as the number of significant digits
2025-03-14T04:33:23.6360137Z     that agree.
2025-03-14T04:33:23.6360302Z     
2025-03-14T04:33:23.6360530Z     Parameters
2025-03-14T04:33:23.6360701Z     ----------
2025-03-14T04:33:23.6360862Z     actual : scalar
2025-03-14T04:33:23.6361052Z         The object to check.
2025-03-14T04:33:23.6361217Z     desired : scalar
2025-03-14T04:33:23.6361410Z         The expected object.
2025-03-14T04:33:23.6361597Z     significant : int, optional
2025-03-14T04:33:23.6361817Z         Desired precision, default is 7.
2025-03-14T04:33:23.6361996Z     err_msg : str, optional
2025-03-14T04:33:23.6362288Z         The error message to be printed in case of failure.
2025-03-14T04:33:23.6362466Z     verbose : bool, optional
2025-03-14T04:33:23.6362850Z         If True, the conflicting values are appended to the error message.
2025-03-14T04:33:23.6363001Z     
2025-03-14T04:33:23.6363172Z     Raises
2025-03-14T04:33:23.6363331Z     ------
2025-03-14T04:33:23.6363505Z     AssertionError
2025-03-14T04:33:23.6363868Z       If actual and desired are not equal up to specified precision.
2025-03-14T04:33:23.6364014Z     
2025-03-14T04:33:23.6364188Z     See Also
2025-03-14T04:33:23.6364341Z     --------
2025-03-14T04:33:23.6364788Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:23.6365012Z                      relative and/or absolute precision.
2025-03-14T04:33:23.6365408Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:23.6365559Z     
2025-03-14T04:33:23.6365727Z     Examples
2025-03-14T04:33:23.6365883Z     --------
2025-03-14T04:33:23.6366427Z     >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)  # doctest: +SKIP
2025-03-14T04:33:23.6366918Z     >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,  # doctest: +SKIP
2025-03-14T04:33:23.6367138Z     ...                                significant=8)
2025-03-14T04:33:23.6367590Z     >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,  # doctest: +SKIP
2025-03-14T04:33:23.6367807Z     ...                                significant=8)
2025-03-14T04:33:23.6368018Z     Traceback (most recent call last):
2025-03-14T04:33:23.6368190Z         ...
2025-03-14T04:33:23.6368376Z     AssertionError:
2025-03-14T04:33:23.6368631Z     Items are not equal to 8 significant digits:
2025-03-14T04:33:23.6368801Z      ACTUAL: 1.234567e-21
2025-03-14T04:33:23.6368983Z      DESIRED: 1.2345672e-21
2025-03-14T04:33:23.6369134Z     
2025-03-14T04:33:23.6369443Z     the evaluated condition that raises the exception is
2025-03-14T04:33:23.6369594Z     
2025-03-14T04:33:23.6369917Z     >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
2025-03-14T04:33:23.6370069Z     True
2025-03-14T04:33:23.6370232Z     
2025-03-14T04:33:23.6370479Z     
2025-03-14T04:33:23.6370953Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6371105Z     
2025-03-14T04:33:23.6371280Z       warnings.warn(msg)
2025-03-14T04:33:23.6371450Z     
2025-03-14T04:33:23.6371788Z --- Parse Warning: 25 / 116 ---
2025-03-14T04:33:23.6373511Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=730.
2025-03-14T04:33:23.6373994Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6374147Z     
2025-03-14T04:33:23.6374522Z     Raises an AssertionError if two array_like objects are not equal.
2025-03-14T04:33:23.6374695Z     
2025-03-14T04:33:23.6375067Z     Given two array_like objects, check that the shape is equal and all
2025-03-14T04:33:23.6375474Z     elements of these objects are equal (but see the Notes for the special
2025-03-14T04:33:23.6375836Z     handling of a scalar). An exception is raised at shape mismatch or
2025-03-14T04:33:23.6376256Z     conflicting values. In contrast to the standard usage in numpy, NaNs
2025-03-14T04:33:23.6376679Z     are compared like numbers, no assertion is raised if both objects have
2025-03-14T04:33:23.6376885Z     NaNs in the same positions.
2025-03-14T04:33:23.6377035Z     
2025-03-14T04:33:23.6377459Z     The usual caution for verifying equality with floating point numbers is
2025-03-14T04:33:23.6377618Z     advised.
2025-03-14T04:33:23.6377779Z     
2025-03-14T04:33:23.6377946Z     Parameters
2025-03-14T04:33:23.6378118Z     ----------
2025-03-14T04:33:23.6378284Z     x : array_like
2025-03-14T04:33:23.6378494Z         The actual object to check.
2025-03-14T04:33:23.6378658Z     y : array_like
2025-03-14T04:33:23.6378876Z         The desired, expected object.
2025-03-14T04:33:23.6379056Z     err_msg : str, optional
2025-03-14T04:33:23.6379357Z         The error message to be printed in case of failure.
2025-03-14T04:33:23.6379541Z     verbose : bool, optional
2025-03-14T04:33:23.6379928Z         If True, the conflicting values are appended to the error message.
2025-03-14T04:33:23.6380107Z     strict : bool, optional
2025-03-14T04:33:23.6380478Z         If True, raise an AssertionError when either the shape or the data
2025-03-14T04:33:23.6380788Z         type of the array_like objects does not match. The special
2025-03-14T04:33:23.6381163Z         handling for scalars mentioned in the Notes section is disabled.
2025-03-14T04:33:23.6381314Z     
2025-03-14T04:33:23.6381479Z     Raises
2025-03-14T04:33:23.6381628Z     ------
2025-03-14T04:33:23.6381809Z     AssertionError
2025-03-14T04:33:23.6382045Z         If actual and desired objects are not equal.
2025-03-14T04:33:23.6382244Z     
2025-03-14T04:33:23.6382430Z     See Also
2025-03-14T04:33:23.6382582Z     --------
2025-03-14T04:33:23.6383025Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:23.6383250Z                      relative and/or absolute precision.
2025-03-14T04:33:23.6383640Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:23.6383794Z     
2025-03-14T04:33:23.6383960Z     Notes
2025-03-14T04:33:23.6384116Z     -----
2025-03-14T04:33:23.6384458Z     When one of `x` and `y` is a scalar and the other is array_like, the
2025-03-14T04:33:23.6384861Z     function checks that each element of the array_like object is equal to
2025-03-14T04:33:23.6385277Z     the scalar. This behaviour can be disabled with the `strict` parameter.
2025-03-14T04:33:23.6385424Z     
2025-03-14T04:33:23.6385593Z     Examples
2025-03-14T04:33:23.6385749Z     --------
2025-03-14T04:33:23.6386001Z     The first assert does not raise an exception:
2025-03-14T04:33:23.6386158Z     
2025-03-14T04:33:23.6386454Z     >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
2025-03-14T04:33:23.6386669Z     ...                               [np.exp(0),2.33333, np.nan])
2025-03-14T04:33:23.6386864Z     
2025-03-14T04:33:23.6387272Z     Use `assert_allclose` or one of the nulp (number of floating point values)
2025-03-14T04:33:23.6387492Z     functions for these cases instead:
2025-03-14T04:33:23.6387640Z     
2025-03-14T04:33:23.6387911Z     >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
2025-03-14T04:33:23.6388131Z     ...                            [1, np.sqrt(np.pi)**2, np.nan],
2025-03-14T04:33:23.6388350Z     ...                            rtol=1e-10, atol=0)
2025-03-14T04:33:23.6388498Z     
2025-03-14T04:33:23.6388883Z     As mentioned in the Notes section, `assert_array_equal` has special
2025-03-14T04:33:23.6389286Z     handling for scalars. Here the test checks that each value in `x` is 3:
2025-03-14T04:33:23.6389449Z     
2025-03-14T04:33:23.6389640Z     >>> x = np.full((2, 5), fill_value=3)
2025-03-14T04:33:23.6389874Z     >>> np.testing.assert_array_equal(x, 3)
2025-03-14T04:33:23.6390024Z     
2025-03-14T04:33:23.6390424Z     Use `strict` to raise an AssertionError when comparing a scalar with an
2025-03-14T04:33:23.6390582Z     array:
2025-03-14T04:33:23.6390748Z     
2025-03-14T04:33:23.6391009Z     >>> np.testing.assert_array_equal(x, 3, strict=True)
2025-03-14T04:33:23.6391210Z     Traceback (most recent call last):
2025-03-14T04:33:23.6391375Z         ...
2025-03-14T04:33:23.6391581Z     AssertionError:
2025-03-14T04:33:23.6391767Z     Arrays are not equal
2025-03-14T04:33:23.6391926Z     <BLANKLINE>
2025-03-14T04:33:23.6392116Z     (shapes (2, 5), () mismatch)
2025-03-14T04:33:23.6392308Z      x: torch.ndarray([[3, 3, 3, 3, 3],
2025-03-14T04:33:23.6392487Z             [3, 3, 3, 3, 3]])
2025-03-14T04:33:23.6392657Z      y: torch.ndarray(3)
2025-03-14T04:33:23.6392826Z     
2025-03-14T04:33:23.6393207Z     The `strict` parameter also ensures that the array data types match:
2025-03-14T04:33:23.6393380Z     
2025-03-14T04:33:23.6393555Z     >>> x = np.array([2, 2, 2])
2025-03-14T04:33:23.6393798Z     >>> y = np.array([2., 2., 2.], dtype=np.float32)
2025-03-14T04:33:23.6394066Z     >>> np.testing.assert_array_equal(x, y, strict=True)
2025-03-14T04:33:23.6394288Z     Traceback (most recent call last):
2025-03-14T04:33:23.6394444Z         ...
2025-03-14T04:33:23.6394634Z     AssertionError:
2025-03-14T04:33:23.6394811Z     Arrays are not equal
2025-03-14T04:33:23.6394989Z     <BLANKLINE>
2025-03-14T04:33:23.6395246Z     (dtypes dtype("int64"), dtype("float32") mismatch)
2025-03-14T04:33:23.6395448Z      x: torch.ndarray([2, 2, 2])
2025-03-14T04:33:23.6395630Z      y: torch.ndarray([2., 2., 2.])
2025-03-14T04:33:23.6395796Z     
2025-03-14T04:33:23.6396249Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6396417Z     
2025-03-14T04:33:23.6396592Z       warnings.warn(msg)
2025-03-14T04:33:23.6396742Z     
2025-03-14T04:33:23.6397163Z --- Parse Warning: 26 / 116 ---
2025-03-14T04:33:23.6398921Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=836.
2025-03-14T04:33:23.6399496Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6399660Z     
2025-03-14T04:33:23.6400048Z     Raises an AssertionError if two objects are not equal up to desired
2025-03-14T04:33:23.6400208Z     precision.
2025-03-14T04:33:23.6400374Z     
2025-03-14T04:33:23.6400675Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-03-14T04:33:23.6401013Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-14T04:33:23.6401346Z               instead of this function for more consistent floating point
2025-03-14T04:33:23.6401531Z               comparisons.
2025-03-14T04:33:23.6401681Z     
2025-03-14T04:33:23.6402130Z     The test verifies identical shapes and that the elements of ``actual`` and
2025-03-14T04:33:23.6402334Z     ``desired`` satisfy.
2025-03-14T04:33:23.6402496Z     
2025-03-14T04:33:23.6402722Z         ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2025-03-14T04:33:23.6402883Z     
2025-03-14T04:33:23.6403293Z     That is a looser test than originally documented, but agrees with what the
2025-03-14T04:33:23.6403743Z     actual implementation did up to rounding vagaries. An exception is raised
2025-03-14T04:33:23.6404161Z     at shape mismatch or conflicting values. In contrast to the standard usage
2025-03-14T04:33:23.6404565Z     in numpy, NaNs are compared like numbers, no assertion is raised if both
2025-03-14T04:33:23.6404782Z     objects have NaNs in the same positions.
2025-03-14T04:33:23.6404944Z     
2025-03-14T04:33:23.6405105Z     Parameters
2025-03-14T04:33:23.6405271Z     ----------
2025-03-14T04:33:23.6405431Z     x : array_like
2025-03-14T04:33:23.6405639Z         The actual object to check.
2025-03-14T04:33:23.6405803Z     y : array_like
2025-03-14T04:33:23.6406013Z         The desired, expected object.
2025-03-14T04:33:23.6406193Z     decimal : int, optional
2025-03-14T04:33:23.6406413Z         Desired precision, default is 6.
2025-03-14T04:33:23.6406589Z     err_msg : str, optional
2025-03-14T04:33:23.6406875Z       The error message to be printed in case of failure.
2025-03-14T04:33:23.6407057Z     verbose : bool, optional
2025-03-14T04:33:23.6407468Z         If True, the conflicting values are appended to the error message.
2025-03-14T04:33:23.6407618Z     
2025-03-14T04:33:23.6407786Z     Raises
2025-03-14T04:33:23.6407940Z     ------
2025-03-14T04:33:23.6408107Z     AssertionError
2025-03-14T04:33:23.6408470Z         If actual and desired are not equal up to specified precision.
2025-03-14T04:33:23.6408621Z     
2025-03-14T04:33:23.6408858Z     See Also
2025-03-14T04:33:23.6409014Z     --------
2025-03-14T04:33:23.6409463Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-14T04:33:23.6409688Z                      relative and/or absolute precision.
2025-03-14T04:33:23.6410077Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-14T04:33:23.6410224Z     
2025-03-14T04:33:23.6422074Z     Examples
2025-03-14T04:33:23.6422314Z     --------
2025-03-14T04:33:23.6422583Z     the first assert does not raise an exception
2025-03-14T04:33:23.6422736Z     
2025-03-14T04:33:23.6423084Z     >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
2025-03-14T04:33:23.6423290Z     ...                                      [1.0,2.333,np.nan])
2025-03-14T04:33:23.6423454Z     
2025-03-14T04:33:23.6423772Z     >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-14T04:33:23.6424012Z     ...                                      [1.0,2.33339,np.nan], decimal=5)
2025-03-14T04:33:23.6424213Z     Traceback (most recent call last):
2025-03-14T04:33:23.6424384Z         ...
2025-03-14T04:33:23.6424715Z     AssertionError:
2025-03-14T04:33:23.6424949Z     Arrays are not almost equal to 5 decimals
2025-03-14T04:33:23.6425105Z     <BLANKLINE>
2025-03-14T04:33:23.6425326Z     Mismatched elements: 1 / 3 (33.3%)
2025-03-14T04:33:23.6425557Z     Max absolute difference: 5.999999999994898e-05
2025-03-14T04:33:23.6425803Z     Max relative difference: 2.5713661239633743e-05
2025-03-14T04:33:23.6426087Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-14T04:33:23.6426397Z      y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2025-03-14T04:33:23.6426544Z     
2025-03-14T04:33:23.6426863Z     >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-14T04:33:23.6427095Z     ...                                      [1.0,2.33333, 5], decimal=5)
2025-03-14T04:33:23.6427297Z     Traceback (most recent call last):
2025-03-14T04:33:23.6427466Z         ...
2025-03-14T04:33:23.6427634Z     AssertionError:
2025-03-14T04:33:23.6427864Z     Arrays are not almost equal to 5 decimals
2025-03-14T04:33:23.6428034Z     <BLANKLINE>
2025-03-14T04:33:23.6428238Z     x and y nan location mismatch:
2025-03-14T04:33:23.6428520Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-14T04:33:23.6428875Z      y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2025-03-14T04:33:23.6429022Z     
2025-03-14T04:33:23.6429187Z     
2025-03-14T04:33:23.6429640Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6429808Z     
2025-03-14T04:33:23.6429989Z       warnings.warn(msg)
2025-03-14T04:33:23.6430154Z     
2025-03-14T04:33:23.6430536Z --- Parse Warning: 27 / 116 ---
2025-03-14T04:33:23.6432311Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1786.
2025-03-14T04:33:23.6432785Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6433189Z     Context manager that resets warning registry for catching warnings
2025-03-14T04:33:23.6433344Z     
2025-03-14T04:33:23.6433798Z         Warnings can be slippery, because, whenever a warning is triggered, Python
2025-03-14T04:33:23.6434206Z         adds a ``__warningregistry__`` member to the *calling* module.  This makes
2025-03-14T04:33:23.6434684Z         it impossible to retrigger the warning in this module, whatever you put in
2025-03-14T04:33:23.6435121Z         the warnings filters.  This context manager accepts a sequence of `modules`
2025-03-14T04:33:23.6435378Z         as a keyword argument to its constructor and:
2025-03-14T04:33:23.6435527Z     
2025-03-14T04:33:23.6435949Z         * stores and removes any ``__warningregistry__`` entries in given `modules`
2025-03-14T04:33:23.6436107Z           on entry;
2025-03-14T04:33:23.6436458Z         * resets ``__warningregistry__`` to its previous state on exit.
2025-03-14T04:33:23.6436612Z     
2025-03-14T04:33:23.6437034Z         This makes it possible to trigger any warning afresh inside the context
2025-03-14T04:33:23.6437359Z         manager without disturbing the state of warnings outside.
2025-03-14T04:33:23.6437531Z     
2025-03-14T04:33:23.6437957Z         For compatibility with Python 3.0, please consider all arguments to be
2025-03-14T04:33:23.6438145Z         keyword-only.
2025-03-14T04:33:23.6438293Z     
2025-03-14T04:33:23.6438473Z         Parameters
2025-03-14T04:33:23.6438632Z         ----------
2025-03-14T04:33:23.6438827Z         record : bool, optional
2025-03-14T04:33:23.6439252Z             Specifies whether warnings should be captured by a custom
2025-03-14T04:33:23.6439692Z             implementation of ``warnings.showwarning()`` and be appended to a list
2025-03-14T04:33:23.6440062Z             returned by the context manager. Otherwise None is returned by the
2025-03-14T04:33:23.6440491Z             context manager. The objects appended to the list are arguments whose
2025-03-14T04:33:23.6440865Z             attributes mirror the arguments to ``showwarning()``.
2025-03-14T04:33:23.6441078Z         modules : sequence, optional
2025-03-14T04:33:23.6441481Z             Sequence of modules for which to reset warnings registry on entry and
2025-03-14T04:33:23.6441841Z             restore on exit. To work correctly, all 'ignore' filters should
2025-03-14T04:33:23.6442041Z             filter by one of these modules.
2025-03-14T04:33:23.6442215Z     
2025-03-14T04:33:23.6442377Z         Examples
2025-03-14T04:33:23.6442553Z         --------
2025-03-14T04:33:23.6442725Z         >>> import warnings
2025-03-14T04:33:23.6443057Z         >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2025-03-14T04:33:23.6443299Z         ...         modules=[np.core.fromnumeric]):
2025-03-14T04:33:23.6443515Z         ...     warnings.simplefilter('always')
2025-03-14T04:33:23.6443936Z         ...     warnings.filterwarnings('ignore', module='np.core.fromnumeric')
2025-03-14T04:33:23.6444252Z         ...     # do something that raises a warning but ignore those in
2025-03-14T04:33:23.6444458Z         ...     # np.core.fromnumeric
2025-03-14T04:33:23.6444644Z         
2025-03-14T04:33:23.6445122Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6445279Z     
2025-03-14T04:33:23.6445472Z       warnings.warn(msg)
2025-03-14T04:33:23.6445622Z     
2025-03-14T04:33:23.6445975Z --- Parse Warning: 28 / 116 ---
2025-03-14T04:33:23.6447680Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354.
2025-03-14T04:33:23.6448171Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6448555Z     Applies a 1D convolution over a quantized input signal composed of
2025-03-14T04:33:23.6448993Z         several quantized input planes.
2025-03-14T04:33:23.6449148Z     
2025-03-14T04:33:23.6449547Z         For details on input arguments, parameters, and implementation see
2025-03-14T04:33:23.6449739Z         :class:`~torch.nn.Conv1d`.
2025-03-14T04:33:23.6449907Z     
2025-03-14T04:33:23.6450077Z         .. note::
2025-03-14T04:33:23.6450449Z             Only `zeros` is supported for the :attr:`padding_mode` argument.
2025-03-14T04:33:23.6450602Z     
2025-03-14T04:33:23.6450778Z         .. note::
2025-03-14T04:33:23.6451183Z             Only `torch.quint8` is supported for the input data type.
2025-03-14T04:33:23.6451356Z     
2025-03-14T04:33:23.6451502Z     
2025-03-14T04:33:23.6451682Z         Attributes:
2025-03-14T04:33:23.6452057Z             weight (Tensor):     packed tensor derived from the learnable weight
2025-03-14T04:33:23.6452254Z                                  parameter.
2025-03-14T04:33:23.6452513Z             scale (Tensor):      scalar for the output scale
2025-03-14T04:33:23.6452825Z             zero_point (Tensor): scalar for the output zero point
2025-03-14T04:33:23.6452977Z     
2025-03-14T04:33:23.6453260Z         See :class:`~torch.nn.Conv1d` for other attributes.
2025-03-14T04:33:23.6453412Z     
2025-03-14T04:33:23.6453598Z         Examples::
2025-03-14T04:33:23.6453745Z     
2025-03-14T04:33:23.6454021Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2025-03-14T04:33:23.6454253Z             >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2025-03-14T04:33:23.6454470Z             >>> input = torch.randn(20, 16, 100)
2025-03-14T04:33:23.6454663Z             >>> # quantize input to quint8
2025-03-14T04:33:23.6454858Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6455235Z             >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2025-03-14T04:33:23.6455476Z             ...                                     dtype=torch.quint8)
2025-03-14T04:33:23.6455660Z             >>> output = m(q_input)
2025-03-14T04:33:23.6455807Z     
2025-03-14T04:33:23.6456029Z         
2025-03-14T04:33:23.6456524Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6456690Z     
2025-03-14T04:33:23.6456870Z       warnings.warn(msg)
2025-03-14T04:33:23.6457035Z     
2025-03-14T04:33:23.6457380Z --- Parse Warning: 29 / 116 ---
2025-03-14T04:33:23.6459048Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11.
2025-03-14T04:33:23.6459523Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6459766Z     A quantized long short-term memory (LSTM).
2025-03-14T04:33:23.6459914Z     
2025-03-14T04:33:23.6460432Z         For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2025-03-14T04:33:23.6460578Z     
2025-03-14T04:33:23.6460755Z         Attributes:
2025-03-14T04:33:23.6460974Z             layers : instances of the `_LSTMLayer`
2025-03-14T04:33:23.6461139Z     
2025-03-14T04:33:23.6461301Z         .. note::
2025-03-14T04:33:23.6461747Z             To access the weights and biases, you need to access them per layer.
2025-03-14T04:33:23.6462052Z             See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2025-03-14T04:33:23.6462215Z     
2025-03-14T04:33:23.6462375Z         Examples::
2025-03-14T04:33:23.6462577Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6462774Z             >>> custom_module_config = {
2025-03-14T04:33:23.6463039Z             ...     'float_to_observed_custom_module_class': {
2025-03-14T04:33:23.6463260Z             ...         nn.LSTM: nn.quantizable.LSTM,
2025-03-14T04:33:23.6463433Z             ...     },
2025-03-14T04:33:23.6463699Z             ...     'observed_to_quantized_custom_module_class': {
2025-03-14T04:33:23.6463963Z             ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2025-03-14T04:33:23.6464117Z             ...     }
2025-03-14T04:33:23.6464290Z             ... }
2025-03-14T04:33:23.6464683Z             >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2025-03-14T04:33:23.6465089Z             >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2025-03-14T04:33:23.6465243Z         
2025-03-14T04:33:23.6465714Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6465866Z     
2025-03-14T04:33:23.6466091Z       warnings.warn(msg)
2025-03-14T04:33:23.6466240Z     
2025-03-14T04:33:23.6466582Z --- Parse Warning: 30 / 116 ---
2025-03-14T04:33:23.6468560Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227.
2025-03-14T04:33:23.6469049Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6469367Z     Squashes the sparse masks into the appropriate tensors.
2025-03-14T04:33:23.6469532Z     
2025-03-14T04:33:23.6469907Z             If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2025-03-14T04:33:23.6470253Z             the module will have a `sparse_params` dict attached to it.
2025-03-14T04:33:23.6470405Z     
2025-03-14T04:33:23.6470577Z             Args:
2025-03-14T04:33:23.6470917Z                 params_to_keep: List of keys to save in the module or a dict
2025-03-14T04:33:23.6471224Z                                 representing the modules and keys that will have
2025-03-14T04:33:23.6471440Z                                 sparsity parameters saved
2025-03-14T04:33:23.6471839Z                 params_to_keep_per_layer: Dict to specify the params that should be
2025-03-14T04:33:23.6472116Z                                 saved for specific layers. The keys in the dict
2025-03-14T04:33:23.6472446Z                                 should be the module fqn, while the values should
2025-03-14T04:33:23.6472769Z                                 be a list of strings with the names of the variables
2025-03-14T04:33:23.6473002Z                                 to save in the `sparse_params`
2025-03-14T04:33:23.6473152Z     
2025-03-14T04:33:23.6473333Z             Examples:
2025-03-14T04:33:23.6473568Z                 >>> # xdoctest: +SKIP("locals are undefined")
2025-03-14T04:33:23.6473793Z                 >>> # Don't save any sparse params
2025-03-14T04:33:23.6474001Z                 >>> sparsifier.squash_mask()
2025-03-14T04:33:23.6474261Z                 >>> hasattr(model.submodule1, 'sparse_params')
2025-03-14T04:33:23.6474416Z                 False
2025-03-14T04:33:23.6474565Z     
2025-03-14T04:33:23.6474787Z                 >>> # Keep sparse params per layer
2025-03-14T04:33:23.6474984Z                 >>> sparsifier.squash_mask(
2025-03-14T04:33:23.6475204Z                 ...     params_to_keep_per_layer={
2025-03-14T04:33:23.6475438Z                 ...         'submodule1.linear1': ('foo', 'bar'),
2025-03-14T04:33:23.6475682Z                 ...         'submodule2.linear42': ('baz',)
2025-03-14T04:33:23.6475874Z                 ...     })
2025-03-14T04:33:23.6476164Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-03-14T04:33:23.6476344Z                 {'foo': 42, 'bar': 24}
2025-03-14T04:33:23.6476643Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-03-14T04:33:23.6476813Z                 {'baz': 0.1}
2025-03-14T04:33:23.6476984Z     
2025-03-14T04:33:23.6477205Z                 >>> # Keep sparse params for all layers
2025-03-14T04:33:23.6477532Z                 >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar'))
2025-03-14T04:33:23.6477802Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-03-14T04:33:23.6477999Z                 {'foo': 42, 'bar': 24}
2025-03-14T04:33:23.6478275Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-03-14T04:33:23.6478478Z                 {'foo': 42, 'bar': 24}
2025-03-14T04:33:23.6478628Z     
2025-03-14T04:33:23.6478997Z                 >>> # Keep some sparse params for all layers, and specific ones for
2025-03-14T04:33:23.6479266Z                 >>> # some other layers
2025-03-14T04:33:23.6479483Z                 >>> sparsifier.squash_mask(
2025-03-14T04:33:23.6479696Z                 ...     params_to_keep=('foo', 'bar'),
2025-03-14T04:33:23.6479917Z                 ...     params_to_keep_per_layer={
2025-03-14T04:33:23.6480176Z                 ...         'submodule2.linear42': ('baz',)
2025-03-14T04:33:23.6480357Z                 ...     })
2025-03-14T04:33:23.6480633Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-03-14T04:33:23.6480818Z                 {'foo': 42, 'bar': 24}
2025-03-14T04:33:23.6481098Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-03-14T04:33:23.6481304Z                 {'foo': 42, 'bar': 24, 'baz': 0.1}
2025-03-14T04:33:23.6481452Z             
2025-03-14T04:33:23.6481932Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6482080Z     
2025-03-14T04:33:23.6482276Z       warnings.warn(msg)
2025-03-14T04:33:23.6482422Z     
2025-03-14T04:33:23.6482779Z --- Parse Warning: 31 / 116 ---
2025-03-14T04:33:23.6484690Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181.
2025-03-14T04:33:23.6485181Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6485331Z     
2025-03-14T04:33:23.6485799Z     Config object that specifies the supported data types passed as arguments to
2025-03-14T04:33:23.6486225Z     quantize ops in the reference model spec, for input and output activations,
2025-03-14T04:33:23.6486415Z     weights, and biases.
2025-03-14T04:33:23.6486655Z     
2025-03-14T04:33:23.6486958Z     For example, consider the following reference model:
2025-03-14T04:33:23.6487108Z     
2025-03-14T04:33:23.6487397Z       quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2025-03-14T04:33:23.6487547Z     
2025-03-14T04:33:23.6487944Z     The pattern in the square brackets refers to the reference pattern of
2025-03-14T04:33:23.6488369Z     statically quantized linear. Setting the input dtype as `torch.quint8`
2025-03-14T04:33:23.6488801Z     in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2025-03-14T04:33:23.6489203Z     to the first quantize op (quant1). Similarly, setting the output dtype as
2025-03-14T04:33:23.6489609Z     `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2025-03-14T04:33:23.6489800Z     the second quantize op (quant2).
2025-03-14T04:33:23.6489961Z     
2025-03-14T04:33:23.6490346Z     Note that the dtype here does not refer to the interface dtypes of the
2025-03-14T04:33:23.6490721Z     op. For example, the "input dtype" here is not the dtype of the input
2025-03-14T04:33:23.6491115Z     tensor passed to the quantized linear op. Though it can still be the
2025-03-14T04:33:23.6491512Z     same as the interface dtype, this is not always the case, e.g. the
2025-03-14T04:33:23.6491920Z     interface dtype is fp32 in dynamic quantization but the "input dtype"
2025-03-14T04:33:23.6492318Z     specified in the DTypeConfig would still be quint8. The semantics of
2025-03-14T04:33:23.6492699Z     dtypes here are the same as the semantics of the dtypes specified in
2025-03-14T04:33:23.6492865Z     the observers.
2025-03-14T04:33:23.6493028Z     
2025-03-14T04:33:23.6493400Z     These dtypes are matched against the ones specified in the user's
2025-03-14T04:33:23.6493798Z     QConfig. If there is a match, and the QConfig satisfies the constraints
2025-03-14T04:33:23.6494195Z     specified in the DTypeConfig (if any), then we will quantize the given
2025-03-14T04:33:23.6494614Z     pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2025-03-14T04:33:23.6494818Z     the pattern will not be quantized.
2025-03-14T04:33:23.6494984Z     
2025-03-14T04:33:23.6495163Z     Example usage::
2025-03-14T04:33:23.6495328Z     
2025-03-14T04:33:23.6495520Z         >>> # xdoctest: +SKIP(failing)
2025-03-14T04:33:23.6495733Z         >>> dtype_config1 = DTypeConfig(
2025-03-14T04:33:23.6495927Z         ...     input_dtype=torch.quint8,
2025-03-14T04:33:23.6496176Z         ...     output_dtype=torch.quint8,
2025-03-14T04:33:23.6496375Z         ...     weight_dtype=torch.qint8,
2025-03-14T04:33:23.6496580Z         ...     bias_dtype=torch.float)
2025-03-14T04:33:23.6496728Z     
2025-03-14T04:33:23.6496941Z         >>> dtype_config2 = DTypeConfig(
2025-03-14T04:33:23.6497164Z         ...     input_dtype=DTypeWithConstraints(
2025-03-14T04:33:23.6497376Z         ...         dtype=torch.quint8,
2025-03-14T04:33:23.6497564Z         ...         quant_min_lower_bound=0,
2025-03-14T04:33:23.6497784Z         ...         quant_max_upper_bound=255,
2025-03-14T04:33:23.6497944Z         ...     ),
2025-03-14T04:33:23.6498189Z         ...     output_dtype=DTypeWithConstraints(
2025-03-14T04:33:23.6498376Z         ...         dtype=torch.quint8,
2025-03-14T04:33:23.6498591Z         ...         quant_min_lower_bound=0,
2025-03-14T04:33:23.6498794Z         ...         quant_max_upper_bound=255,
2025-03-14T04:33:23.6498964Z         ...     ),
2025-03-14T04:33:23.6499188Z         ...     weight_dtype=DTypeWithConstraints(
2025-03-14T04:33:23.6499392Z         ...         dtype=torch.qint8,
2025-03-14T04:33:23.6499597Z         ...         quant_min_lower_bound=-128,
2025-03-14T04:33:23.6499816Z         ...         quant_max_upper_bound=127,
2025-03-14T04:33:23.6499969Z         ...     ),
2025-03-14T04:33:23.6500173Z         ...     bias_dtype=torch.float)
2025-03-14T04:33:23.6500327Z     
2025-03-14T04:33:23.6500525Z         >>> dtype_config1.input_dtype
2025-03-14T04:33:23.6500704Z         torch.quint8
2025-03-14T04:33:23.6500892Z     
2025-03-14T04:33:23.6501140Z         >>> dtype_config2.input_dtype
2025-03-14T04:33:23.6501306Z         torch.quint8
2025-03-14T04:33:23.6501471Z     
2025-03-14T04:33:23.6501720Z         >>> dtype_config2.input_dtype_with_constraints
2025-03-14T04:33:23.6502721Z         DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2025-03-14T04:33:23.6502871Z     
2025-03-14T04:33:23.6503347Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6503497Z     
2025-03-14T04:33:23.6503683Z       warnings.warn(msg)
2025-03-14T04:33:23.6503830Z     
2025-03-14T04:33:23.6504183Z --- Parse Warning: 32 / 116 ---
2025-03-14T04:33:23.6506498Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301.
2025-03-14T04:33:23.6506991Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6507176Z     
2025-03-14T04:33:23.6507678Z     Takes in optional filter values and generates two tables with desired information.
2025-03-14T04:33:23.6507825Z     
2025-03-14T04:33:23.6508304Z     The generated tables are presented in both a list-of-lists format
2025-03-14T04:33:23.6508453Z     
2025-03-14T04:33:23.6508841Z     The reason for the two tables are that they handle different things:
2025-03-14T04:33:23.6509129Z     1.) the first table handles all tensor level information
2025-03-14T04:33:23.6509533Z     2.) the second table handles and displays all channel based information
2025-03-14T04:33:23.6509679Z     
2025-03-14T04:33:23.6510275Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-14T04:33:23.6510875Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-14T04:33:23.6511533Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-14T04:33:23.6511683Z     
2025-03-14T04:33:23.6511877Z     Tensor table columns:
2025-03-14T04:33:23.6512214Z         idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:23.6512535Z         ----  ---------  ---------   ---------   ---------        ---------
2025-03-14T04:33:23.6512688Z     
2025-03-14T04:33:23.6512894Z     Per-Channel table columns:
2025-03-14T04:33:23.6513288Z         idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:23.6513594Z         ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-14T04:33:23.6513740Z     
2025-03-14T04:33:23.6513910Z     Args:
2025-03-14T04:33:23.6514384Z         feature_filter (str, optional): Filters the features presented to only those that
2025-03-14T04:33:23.6514602Z             contain this filter substring
2025-03-14T04:33:23.6514893Z             Default = "", results in all the features being printed
2025-03-14T04:33:23.6515382Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:23.6515825Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:23.6515986Z     
2025-03-14T04:33:23.6516197Z     Returns a dictionary with two keys:
2025-03-14T04:33:23.6516520Z         (Dict[str, Tuple[List, List]]) A dict containing two keys:
2025-03-14T04:33:23.6516740Z         "tensor_level_info", "channel_level_info"
2025-03-14T04:33:23.6516957Z             Each key maps to a tuple with:
2025-03-14T04:33:23.6517168Z                 A list of the headers of each table
2025-03-14T04:33:23.6517503Z                 A list of lists containing the table information row by row
2025-03-14T04:33:23.6517843Z                 The 0th index row will contain the headers of the columns
2025-03-14T04:33:23.6518112Z                 The rest of the rows will contain data
2025-03-14T04:33:23.6518262Z     
2025-03-14T04:33:23.6518442Z     Example Use:
2025-03-14T04:33:23.6518673Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6518951Z         >>> mod_report_visualizer.generate_filtered_tables(
2025-03-14T04:33:23.6519245Z         ...     feature_filter = "per_channel_min",
2025-03-14T04:33:23.6519458Z         ...     module_fqn_filter = "block1"
2025-03-14T04:33:23.6519951Z         ... ) # generates table with per_channel_min info for all modules in block 1 of the model
2025-03-14T04:33:23.6520116Z     
2025-03-14T04:33:23.6520570Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6520730Z     
2025-03-14T04:33:23.6520903Z       warnings.warn(msg)
2025-03-14T04:33:23.6521069Z     
2025-03-14T04:33:23.6521412Z --- Parse Warning: 33 / 116 ---
2025-03-14T04:33:23.6523803Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400.
2025-03-14T04:33:23.6524320Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6524465Z     
2025-03-14T04:33:23.6524969Z     Takes in optional filter values and prints out formatted tables of the information.
2025-03-14T04:33:23.6525120Z     
2025-03-14T04:33:23.6525750Z     The reason for the two tables printed out instead of one large one are that they handle different things:
2025-03-14T04:33:23.6526039Z     1.) the first table handles all tensor level information
2025-03-14T04:33:23.6526436Z     2.) the second table handles and displays all channel based information
2025-03-14T04:33:23.6526585Z     
2025-03-14T04:33:23.6527176Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-14T04:33:23.6527771Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-14T04:33:23.6528422Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-14T04:33:23.6528564Z     
2025-03-14T04:33:23.6528746Z     Tensor table columns:
2025-03-14T04:33:23.6529115Z      idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:23.6529389Z     ----  ---------  ---------   ---------   ---------        ---------
2025-03-14T04:33:23.6529541Z     
2025-03-14T04:33:23.6529744Z     Per-Channel table columns:
2025-03-14T04:33:23.6529891Z     
2025-03-14T04:33:23.6530288Z      idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-14T04:33:23.6530571Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-14T04:33:23.6530733Z     
2025-03-14T04:33:23.6530883Z     Args:
2025-03-14T04:33:23.6531354Z         feature_filter (str, optional): Filters the features presented to only those that
2025-03-14T04:33:23.6531555Z             contain this filter substring
2025-03-14T04:33:23.6531842Z             Default = "", results in all the features being printed
2025-03-14T04:33:23.6532323Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:23.6532781Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:23.6532920Z     
2025-03-14T04:33:23.6533079Z     Example Use:
2025-03-14T04:33:23.6533310Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6533597Z         >>> mod_report_visualizer.generate_table_visualization(
2025-03-14T04:33:23.6533827Z         ...     feature_filter = "per_channel_min",
2025-03-14T04:33:23.6534016Z         ...     module_fqn_filter = "block1"
2025-03-14T04:33:23.6534264Z         ... )
2025-03-14T04:33:23.6534639Z         >>> # prints out neatly formatted table with per_channel_min info
2025-03-14T04:33:23.6534869Z         >>> # for all modules in block 1 of the model
2025-03-14T04:33:23.6535018Z     
2025-03-14T04:33:23.6535486Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6535636Z     
2025-03-14T04:33:23.6535821Z       warnings.warn(msg)
2025-03-14T04:33:23.6535963Z     
2025-03-14T04:33:23.6536306Z --- Parse Warning: 34 / 116 ---
2025-03-14T04:33:23.6538644Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566.
2025-03-14T04:33:23.6539125Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6539262Z     
2025-03-14T04:33:23.6539701Z     Takes in a feature and optional module_filter and plots of the desired data.
2025-03-14T04:33:23.6539850Z     
2025-03-14T04:33:23.6540381Z     For per channel features, it averages the value across the channels and plots a point
2025-03-14T04:33:23.6540842Z     per module. The reason for this is that for models with hundreds of channels, it can
2025-03-14T04:33:23.6541347Z     be hard to differentiate one channel line from another, and so the point of generating
2025-03-14T04:33:23.6541827Z     a single average point per module is to give a sense of general trends that encourage
2025-03-14T04:33:23.6542010Z     further deep dives.
2025-03-14T04:33:23.6542156Z     
2025-03-14T04:33:23.6542318Z     Note:
2025-03-14T04:33:23.6542790Z         Only features in the report that have tensor value data are plottable by this class
2025-03-14T04:33:23.6543092Z         When the tensor information is plotted, it will plot:
2025-03-14T04:33:23.6543330Z             idx as the x val, feature value as the y_val
2025-03-14T04:33:23.6543637Z         When the channel information is plotted, it will plot:
2025-03-14T04:33:23.6544109Z             the first idx of each module as the x val, feature value as the y_val [for each channel]
2025-03-14T04:33:23.6544525Z             The reason for this is that we want to be able to compare values across the
2025-03-14T04:33:23.6544939Z             channels for same layer, and it will be hard if values are staggered by idx
2025-03-14T04:33:23.6545282Z             This means each module is represented by only 1 x value
2025-03-14T04:33:23.6545435Z     Args:
2025-03-14T04:33:23.6545842Z         feature_filter (str): Filters the features presented to only those that
2025-03-14T04:33:23.6546034Z             contain this filter substring
2025-03-14T04:33:23.6546512Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:23.6546950Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:23.6547110Z     
2025-03-14T04:33:23.6547262Z     Example Use:
2025-03-14T04:33:23.6547496Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6547780Z         >>> mod_report_visualizer.generate_plot_visualization(
2025-03-14T04:33:23.6548010Z         ...     feature_filter = "per_channel_min",
2025-03-14T04:33:23.6548198Z         ...     module_fqn_filter = "block1"
2025-03-14T04:33:23.6548362Z         ... )
2025-03-14T04:33:23.6548831Z         >>> # outputs line plot of per_channel_min information for all
2025-03-14T04:33:23.6549177Z         >>> # modules in block1 of model each channel gets it's own line,
2025-03-14T04:33:23.6549477Z         >>> # and it's plotted across the in-order modules on the x-axis
2025-03-14T04:33:23.6549639Z     
2025-03-14T04:33:23.6550093Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6550248Z     
2025-03-14T04:33:23.6550420Z       warnings.warn(msg)
2025-03-14T04:33:23.6550686Z     
2025-03-14T04:33:23.6551071Z --- Parse Warning: 35 / 116 ---
2025-03-14T04:33:23.6553483Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646.
2025-03-14T04:33:23.6553961Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6554121Z     
2025-03-14T04:33:23.6554623Z     Takes in a feature and optional module_filter and plots the histogram of desired data.
2025-03-14T04:33:23.6554779Z     
2025-03-14T04:33:23.6554934Z     Note:
2025-03-14T04:33:23.6555431Z         Only features in the report that have tensor value data can be viewed as a histogram
2025-03-14T04:33:23.6555898Z         If you want to plot a histogram from all the channel values of a specific feature for
2025-03-14T04:33:23.6556359Z             a specific model, make sure to specify both the model and the feature properly
2025-03-14T04:33:23.6556795Z             in the filters and you should be able to see a distribution of the channel data
2025-03-14T04:33:23.6556996Z     
2025-03-14T04:33:23.6557149Z     Args:
2025-03-14T04:33:23.6557630Z         feature_filter (str, optional): Filters the features presented to only those that
2025-03-14T04:33:23.6557831Z             contain this filter substring
2025-03-14T04:33:23.6558134Z             Default = "", results in all the features being printed
2025-03-14T04:33:23.6558599Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-14T04:33:23.6559049Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-14T04:33:23.6559528Z         num_bins (int, optional): The number of bins to create the histogram with
2025-03-14T04:33:23.6559875Z             Default = 10, the values will be split into 10 equal sized bins
2025-03-14T04:33:23.6560024Z     
2025-03-14T04:33:23.6560193Z     Example Use:
2025-03-14T04:33:23.6560367Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6560926Z         >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2025-03-14T04:33:23.6561143Z         ...     feature_filter = "per_channel_min",
2025-03-14T04:33:23.6561345Z         ...     module_fqn_filter = "block1"
2025-03-14T04:33:23.6561495Z         ... )
2025-03-14T04:33:23.6562054Z         # outputs histogram of per_channel_min information for all modules in block1 of model
2025-03-14T04:33:23.6562518Z             information is gathered across all channels for all modules in block 1 for the
2025-03-14T04:33:23.6562915Z             per_channel_min and is displayed in a histogram of equally sized bins
2025-03-14T04:33:23.6563063Z     
2025-03-14T04:33:23.6563522Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6563669Z     
2025-03-14T04:33:23.6563852Z       warnings.warn(msg)
2025-03-14T04:33:23.6564001Z     
2025-03-14T04:33:23.6564329Z --- Parse Warning: 36 / 116 ---
2025-03-14T04:33:23.6566099Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=666.
2025-03-14T04:33:23.6566551Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-14T04:33:23.6566693Z     
2025-03-14T04:33:23.6567178Z     Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2025-03-14T04:33:23.6567653Z     The submesh created consists of the dimensions and the communicators indicated by
2025-03-14T04:33:23.6567824Z     ``mesh_dim_names``
2025-03-14T04:33:23.6567968Z     
2025-03-14T04:33:23.6568114Z     Args:
2025-03-14T04:33:23.6568579Z         mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2025-03-14T04:33:23.6568937Z         mesh dimension of the DeviceMesh to create the submesh for.
2025-03-14T04:33:23.6569106Z     Returns:
2025-03-14T04:33:23.6569296Z         A :class:`DeviceMesh` object
2025-03-14T04:33:23.6569452Z     
2025-03-14T04:33:23.6569959Z     The following program runs on each process/rank in an SPMD manner in a world size of 8.
2025-03-14T04:33:23.6570149Z     In the first example:
2025-03-14T04:33:23.6570590Z         Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2025-03-14T04:33:23.6571048Z         Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2025-03-14T04:33:23.6571452Z         Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2025-03-14T04:33:23.6571859Z         Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2025-03-14T04:33:23.6572256Z         Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2025-03-14T04:33:23.6572660Z         Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2025-03-14T04:33:23.6572846Z     
2025-03-14T04:33:23.6573032Z     In the second example:
2025-03-14T04:33:23.6573504Z         Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2025-03-14T04:33:23.6573988Z         Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2025-03-14T04:33:23.6574454Z         Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2025-03-14T04:33:23.6574935Z         Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2025-03-14T04:33:23.6575082Z     
2025-03-14T04:33:23.6575251Z     Example::
2025-03-14T04:33:23.6575441Z         >>> # xdoctest: +SKIP("no rank")
2025-03-14T04:33:23.6575749Z         >>> from torch.distributed.device_mesh import DeviceMesh
2025-03-14T04:33:23.6575905Z         >>>
2025-03-14T04:33:23.6576269Z         >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2025-03-14T04:33:23.6576517Z         >>> # of cross-host(dim 0), and within-host (dim 1).
2025-03-14T04:33:23.6576979Z         >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-14T04:33:23.6577157Z         >>> tp_mesh = mesh_2d["tp"]
2025-03-14T04:33:23.6577343Z         >>> dp_mesh = mesh_2d["dp"]
2025-03-14T04:33:23.6577524Z         >>>
2025-03-14T04:33:23.6577714Z         >>> # Initialize a 3D mesh.
2025-03-14T04:33:23.6578206Z         >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2025-03-14T04:33:23.6578761Z         >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2025-03-14T04:33:23.6578964Z         >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2025-03-14T04:33:23.6579178Z         >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2025-03-14T04:33:23.6579330Z     
2025-03-14T04:33:23.6580583Z     Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2025-03-14T04:33:23.6580734Z     
2025-03-14T04:33:23.6581190Z     mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-14T04:33:23.6581402Z                                                                                      ^
2025-03-14T04:33:23.6581596Z       warnings.warn(msg)
2025-03-14T04:33:23.6581744Z     
2025-03-14T04:33:23.6582089Z --- Parse Warning: 37 / 116 ---
2025-03-14T04:33:23.6583865Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2604.
2025-03-14T04:33:23.6584387Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6584567Z     
2025-03-14T04:33:23.6585034Z     Send or Receive a batch of tensors asynchronously and return a list of requests.
2025-03-14T04:33:23.6585188Z     
2025-03-14T04:33:23.6585646Z     Process each of the operations in ``p2p_op_list`` and return the corresponding
2025-03-14T04:33:23.6585993Z     requests. NCCL, Gloo, and UCC backend are currently supported.
2025-03-14T04:33:23.6586161Z     
2025-03-14T04:33:23.6586321Z     Args:
2025-03-14T04:33:23.6586741Z         p2p_op_list: A list of point-to-point operations(type of each operator is
2025-03-14T04:33:23.6587148Z             ``torch.distributed.P2POp``). The order of the isend/irecv in the list
2025-03-14T04:33:23.6587541Z             matters and it needs to match with corresponding isend/irecv on the
2025-03-14T04:33:23.6587709Z             remote end.
2025-03-14T04:33:23.6587877Z     
2025-03-14T04:33:23.6588035Z     Returns:
2025-03-14T04:33:23.6588492Z         A list of distributed request objects returned by calling the corresponding
2025-03-14T04:33:23.6588674Z         op in the op_list.
2025-03-14T04:33:23.6588844Z     
2025-03-14T04:33:23.6589032Z     Examples:
2025-03-14T04:33:23.6589247Z         >>> # xdoctest: +SKIP("no rank")
2025-03-14T04:33:23.6589586Z         >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
2025-03-14T04:33:23.6589868Z         >>> recv_tensor = torch.randn(2, dtype=torch.float32)
2025-03-14T04:33:23.6590250Z         >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
2025-03-14T04:33:23.6590449Z         >>> recv_op = dist.P2POp(
2025-03-14T04:33:23.6590784Z         ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
2025-03-14T04:33:23.6590955Z         ... )
2025-03-14T04:33:23.6591195Z         >>> reqs = batch_isend_irecv([send_op, recv_op])
2025-03-14T04:33:23.6591386Z         >>> for req in reqs:
2025-03-14T04:33:23.6591557Z         >>>     req.wait()
2025-03-14T04:33:23.6591735Z         >>> recv_tensor
2025-03-14T04:33:23.6591916Z         tensor([2, 3])     # Rank 0
2025-03-14T04:33:23.6592106Z         tensor([0, 1])     # Rank 1
2025-03-14T04:33:23.6592258Z     
2025-03-14T04:33:23.6592697Z     .. note:: Note that when this API is used with the NCCL PG backend, users must set
2025-03-14T04:33:23.6593118Z         the current GPU device with `torch.cuda.set_device`, otherwise it will
2025-03-14T04:33:23.6593317Z         lead to unexpected hang issues.
2025-03-14T04:33:23.6593518Z     
2025-03-14T04:33:23.6593895Z         In addition, if this API is the first collective call in the ``group``
2025-03-14T04:33:23.6594305Z         passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
2025-03-14T04:33:23.6594704Z         this API call; otherwise, the behavior is undefined. If this API call is
2025-03-14T04:33:23.6595117Z         not the first collective call in the ``group``, batched P2P operations
2025-03-14T04:33:23.6595471Z         involving only a subset of ranks of the ``group`` are allowed.
2025-03-14T04:33:23.6595640Z     
2025-03-14T04:33:23.6596099Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6596262Z     
2025-03-14T04:33:23.6596433Z       warnings.warn(msg)
2025-03-14T04:33:23.6596591Z     
2025-03-14T04:33:23.6596913Z --- Parse Warning: 38 / 116 ---
2025-03-14T04:33:23.6598647Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2734.
2025-03-14T04:33:23.6599198Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6599366Z     
2025-03-14T04:33:23.6599847Z     Reduces the tensor data across all machines in a way that all get the final result.
2025-03-14T04:33:23.6600009Z     
2025-03-14T04:33:23.6600420Z     After the call ``tensor`` is going to be bitwise identical in all processes.
2025-03-14T04:33:23.6600660Z     
2025-03-14T04:33:23.6600860Z     Complex tensors are supported.
2025-03-14T04:33:23.6601025Z     
2025-03-14T04:33:23.6601184Z     Args:
2025-03-14T04:33:23.6601563Z         tensor (Tensor): Input and output of the collective. The function
2025-03-14T04:33:23.6601743Z             operates in-place.
2025-03-14T04:33:23.6601965Z         op (optional): One of the values from
2025-03-14T04:33:23.6602176Z             ``torch.distributed.ReduceOp``
2025-03-14T04:33:23.6602549Z             enum.  Specifies an operation used for element-wise reductions.
2025-03-14T04:33:23.6602964Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:23.6603201Z             the default process group will be used.
2025-03-14T04:33:23.6603548Z         async_op (bool, optional): Whether this op should be an async op
2025-03-14T04:33:23.6603711Z     
2025-03-14T04:33:23.6603870Z     Returns:
2025-03-14T04:33:23.6604136Z         Async work handle, if async_op is set to True.
2025-03-14T04:33:23.6604398Z         None, if not async_op or if not part of the group
2025-03-14T04:33:23.6604558Z     
2025-03-14T04:33:23.6604749Z     Examples:
2025-03-14T04:33:23.6604953Z         >>> # xdoctest: +SKIP("no rank")
2025-03-14T04:33:23.6605188Z         >>> # All tensors below are of torch.int64 type.
2025-03-14T04:33:23.6605407Z         >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:23.6605617Z         >>> device = torch.device(f"cuda:{rank}")
2025-03-14T04:33:23.6606042Z         >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-14T04:33:23.6606201Z         >>> tensor
2025-03-14T04:33:23.6606424Z         tensor([1, 2], device='cuda:0') # Rank 0
2025-03-14T04:33:23.6606630Z         tensor([3, 4], device='cuda:1') # Rank 1
2025-03-14T04:33:23.6606877Z         >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-14T04:33:23.6607032Z         >>> tensor
2025-03-14T04:33:23.6607254Z         tensor([4, 6], device='cuda:0') # Rank 0
2025-03-14T04:33:23.6607459Z         tensor([4, 6], device='cuda:1') # Rank 1
2025-03-14T04:33:23.6607611Z     
2025-03-14T04:33:23.6607869Z         >>> # All tensors below are of torch.cfloat type.
2025-03-14T04:33:23.6608083Z         >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:23.6608285Z         >>> tensor = torch.tensor(
2025-03-14T04:33:23.6608536Z         ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-14T04:33:23.6608778Z         ... ) + 2 * rank * (1 + 1j)
2025-03-14T04:33:23.6608937Z         >>> tensor
2025-03-14T04:33:23.6609199Z         tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-14T04:33:23.6609443Z         tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-14T04:33:23.6609693Z         >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-14T04:33:23.6609849Z         >>> tensor
2025-03-14T04:33:23.6610106Z         tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
2025-03-14T04:33:23.6610351Z         tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
2025-03-14T04:33:23.6610523Z     
2025-03-14T04:33:23.6610674Z     
2025-03-14T04:33:23.6611150Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6611309Z     
2025-03-14T04:33:23.6611502Z       warnings.warn(msg)
2025-03-14T04:33:23.6611653Z     
2025-03-14T04:33:23.6612054Z --- Parse Warning: 39 / 116 ---
2025-03-14T04:33:23.6613812Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3090.
2025-03-14T04:33:23.6614304Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6614455Z     
2025-03-14T04:33:23.6614874Z     Gathers picklable objects from the whole group in a single process.
2025-03-14T04:33:23.6615025Z     
2025-03-14T04:33:23.6615494Z     Similar to :func:`gather`, but Python objects can be passed in. Note that the
2025-03-14T04:33:23.6615786Z     object must be picklable in order to be gathered.
2025-03-14T04:33:23.6615958Z     
2025-03-14T04:33:23.6616113Z     Args:
2025-03-14T04:33:23.6616364Z         obj (Any): Input object. Must be picklable.
2025-03-14T04:33:23.6616742Z         object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2025-03-14T04:33:23.6617089Z             should be correctly sized as the size of the group for this
2025-03-14T04:33:23.6617483Z             collective and will contain the output. Must be ``None`` on non-dst
2025-03-14T04:33:23.6617691Z             ranks. (default is ``None``)
2025-03-14T04:33:23.6618271Z         dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
2025-03-14T04:33:23.6618630Z             (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
2025-03-14T04:33:23.6619046Z         group: (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:23.6619410Z             the default process group will be used. Default is ``None``.
2025-03-14T04:33:23.6620030Z         group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
2025-03-14T04:33:23.6620229Z     
2025-03-14T04:33:23.6620393Z     Returns:
2025-03-14T04:33:23.6620730Z         None. On the ``dst`` rank, ``object_gather_list`` will contain the
2025-03-14T04:33:23.6620920Z         output of the collective.
2025-03-14T04:33:23.6621088Z     
2025-03-14T04:33:23.6621472Z     .. note:: Note that this API differs slightly from the gather collective
2025-03-14T04:33:23.6621874Z         since it does not provide an async_op handle and thus will be a blocking
2025-03-14T04:33:23.6622028Z         call.
2025-03-14T04:33:23.6622193Z     
2025-03-14T04:33:23.6622601Z     .. note:: For NCCL-based processed groups, internal tensor representations
2025-03-14T04:33:23.6622987Z         of objects must be moved to the GPU device before communication takes
2025-03-14T04:33:23.6623247Z         place. In this case, the device used is given by
2025-03-14T04:33:23.6623651Z         ``torch.cuda.current_device()`` and it is the user's responsiblity to
2025-03-14T04:33:23.6624024Z         ensure that this is set so that each rank has an individual GPU, via
2025-03-14T04:33:23.6624231Z         ``torch.cuda.set_device()``.
2025-03-14T04:33:23.6624381Z     
2025-03-14T04:33:23.6624549Z     .. warning::
2025-03-14T04:33:23.6624929Z         :func:`gather_object` uses ``pickle`` module implicitly, which is
2025-03-14T04:33:23.6625332Z         known to be insecure. It is possible to construct malicious pickle data
2025-03-14T04:33:23.6625721Z         which will execute arbitrary code during unpickling. Only call this
2025-03-14T04:33:23.6625934Z         function with data you trust.
2025-03-14T04:33:23.6626082Z     
2025-03-14T04:33:23.6626256Z     .. warning::
2025-03-14T04:33:23.6626638Z         Calling :func:`gather_object` with GPU tensors is not well supported
2025-03-14T04:33:23.6627057Z         and inefficient as it incurs GPU -> CPU transfer since tensors would be
2025-03-14T04:33:23.6627357Z         pickled. Please consider using :func:`gather` instead.
2025-03-14T04:33:23.6627506Z     
2025-03-14T04:33:23.6627682Z     Example::
2025-03-14T04:33:23.6627924Z         >>> # xdoctest: +SKIP("need process group init")
2025-03-14T04:33:23.6628254Z         >>> # Note: Process group initialization omitted on each rank.
2025-03-14T04:33:23.6628466Z         >>> import torch.distributed as dist
2025-03-14T04:33:23.6628674Z         >>> # Assumes world_size of 3.
2025-03-14T04:33:23.6628983Z         >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2025-03-14T04:33:23.6629211Z         >>> output = [None for _ in gather_objects]
2025-03-14T04:33:23.6629388Z         >>> dist.gather_object(
2025-03-14T04:33:23.6629614Z         ...     gather_objects[dist.get_rank()],
2025-03-14T04:33:23.6629828Z         ...     output if dist.get_rank() == 0 else None,
2025-03-14T04:33:23.6630097Z         ...     dst=0
2025-03-14T04:33:23.6630189Z         ... )
2025-03-14T04:33:23.6630297Z         >>> # On rank 0
2025-03-14T04:33:23.6630389Z         >>> output
2025-03-14T04:33:23.6630504Z         ['foo', 12, {1: 2}]
2025-03-14T04:33:23.6630593Z     
2025-03-14T04:33:23.6630867Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6630956Z     
2025-03-14T04:33:23.6631076Z       warnings.warn(msg)
2025-03-14T04:33:23.6631165Z     
2025-03-14T04:33:23.6631398Z --- Parse Warning: 40 / 116 ---
2025-03-14T04:33:23.6632323Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3666.
2025-03-14T04:33:23.6632609Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6632697Z     
2025-03-14T04:33:23.6632858Z     Gathers tensors from the whole group in a list.
2025-03-14T04:33:23.6632948Z     
2025-03-14T04:33:23.6633114Z     Complex and uneven sized tensors are supported.
2025-03-14T04:33:23.6633232Z     
2025-03-14T04:33:23.6633336Z     Args:
2025-03-14T04:33:23.6633520Z         tensor_list (list[Tensor]): Output list. It should contain
2025-03-14T04:33:23.6633755Z             correctly-sized tensors to be used for output of the collective.
2025-03-14T04:33:23.6633888Z             Uneven sized tensors are supported.
2025-03-14T04:33:23.6634096Z         tensor (Tensor): Tensor to be broadcast from current process.
2025-03-14T04:33:23.6634332Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:23.6634478Z             the default process group will be used.
2025-03-14T04:33:23.6634679Z         async_op (bool, optional): Whether this op should be an async op
2025-03-14T04:33:23.6634780Z     
2025-03-14T04:33:23.6634874Z     Returns:
2025-03-14T04:33:23.6635037Z         Async work handle, if async_op is set to True.
2025-03-14T04:33:23.6635192Z         None, if not async_op or if not part of the group
2025-03-14T04:33:23.6635297Z     
2025-03-14T04:33:23.6635395Z     Examples:
2025-03-14T04:33:23.6635538Z         >>> # xdoctest: +SKIP("need process group init")
2025-03-14T04:33:23.6635695Z         >>> # All tensors below are of torch.int64 dtype.
2025-03-14T04:33:23.6635820Z         >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:23.6635987Z         >>> device = torch.device(f"cuda:{rank}")
2025-03-14T04:33:23.6636093Z         >>> tensor_list = [
2025-03-14T04:33:23.6636324Z         ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
2025-03-14T04:33:23.6636415Z         ... ]
2025-03-14T04:33:23.6636530Z         >>> tensor_list
2025-03-14T04:33:23.6636732Z         [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
2025-03-14T04:33:23.6636949Z         [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
2025-03-14T04:33:23.6637185Z         >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-14T04:33:23.6637296Z         >>> tensor
2025-03-14T04:33:23.6637419Z         tensor([1, 2], device='cuda:0') # Rank 0
2025-03-14T04:33:23.6637552Z         tensor([3, 4], device='cuda:1') # Rank 1
2025-03-14T04:33:23.6637677Z         >>> dist.all_gather(tensor_list, tensor)
2025-03-14T04:33:23.6637791Z         >>> tensor_list
2025-03-14T04:33:23.6637992Z         [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
2025-03-14T04:33:23.6638200Z         [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
2025-03-14T04:33:23.6638288Z     
2025-03-14T04:33:23.6638452Z         >>> # All tensors below are of torch.cfloat dtype.
2025-03-14T04:33:23.6638574Z         >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:23.6638685Z         >>> tensor_list = [
2025-03-14T04:33:23.6638931Z         ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
2025-03-14T04:33:23.6639053Z         ... ]
2025-03-14T04:33:23.6639240Z         >>> tensor_list
2025-03-14T04:33:23.6639510Z         [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
2025-03-14T04:33:23.6639756Z         [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
2025-03-14T04:33:23.6639880Z         >>> tensor = torch.tensor(
2025-03-14T04:33:23.6640033Z         ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-14T04:33:23.6640149Z         ... ) + 2 * rank * (1 + 1j)
2025-03-14T04:33:23.6640239Z         >>> tensor
2025-03-14T04:33:23.6640395Z         tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-14T04:33:23.6640536Z         tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-14T04:33:23.6640674Z         >>> dist.all_gather(tensor_list, tensor)
2025-03-14T04:33:23.6640770Z         >>> tensor_list
2025-03-14T04:33:23.6641028Z         [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
2025-03-14T04:33:23.6641271Z         [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
2025-03-14T04:33:23.6641400Z     
2025-03-14T04:33:23.6641487Z     
2025-03-14T04:33:23.6641756Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6641842Z     
2025-03-14T04:33:23.6641963Z       warnings.warn(msg)
2025-03-14T04:33:23.6642048Z     
2025-03-14T04:33:23.6642269Z --- Parse Warning: 41 / 116 ---
2025-03-14T04:33:23.6643223Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4381.
2025-03-14T04:33:23.6643506Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6643593Z     
2025-03-14T04:33:23.6643863Z     Split input tensor and then scatter the split list to all processes in a group.
2025-03-14T04:33:23.6643951Z     
2025-03-14T04:33:23.6644226Z     Later the received tensors are concatenated from all the processes in the group
2025-03-14T04:33:23.6644356Z     and returned as a single output tensor.
2025-03-14T04:33:23.6644459Z     
2025-03-14T04:33:23.6644573Z     Complex tensors are supported.
2025-03-14T04:33:23.6644669Z     
2025-03-14T04:33:23.6644758Z     Args:
2025-03-14T04:33:23.6644957Z         output (Tensor): Gathered concatenated output tensor.
2025-03-14T04:33:23.6645101Z         input (Tensor): Input tensor to scatter.
2025-03-14T04:33:23.6645328Z         output_split_sizes: (list[Int], optional): Output split sizes for dim 0
2025-03-14T04:33:23.6645553Z             if specified None or empty, dim 0 of ``output`` tensor must divide
2025-03-14T04:33:23.6645665Z             equally by ``world_size``.
2025-03-14T04:33:23.6645896Z         input_split_sizes: (list[Int], optional): Input split sizes for dim 0
2025-03-14T04:33:23.6646103Z             if specified None or empty, dim 0 of ``input`` tensor must divide
2025-03-14T04:33:23.6646225Z             equally by ``world_size``.
2025-03-14T04:33:23.6646463Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:23.6646608Z             the default process group will be used.
2025-03-14T04:33:23.6646807Z         async_op (bool, optional): Whether this op should be an async op.
2025-03-14T04:33:23.6646909Z     
2025-03-14T04:33:23.6647000Z     Returns:
2025-03-14T04:33:23.6647160Z         Async work handle, if async_op is set to True.
2025-03-14T04:33:23.6647312Z         None, if not async_op or if not part of the group.
2025-03-14T04:33:23.6647412Z     
2025-03-14T04:33:23.6647512Z     .. warning::
2025-03-14T04:33:23.6647702Z         `all_to_all_single` is experimental and subject to change.
2025-03-14T04:33:23.6647789Z     
2025-03-14T04:33:23.6647896Z     Examples:
2025-03-14T04:33:23.6648047Z         >>> # xdoctest: +SKIP("Undefined rank")
2025-03-14T04:33:23.6648206Z         >>> input = torch.arange(4) + rank * 4
2025-03-14T04:33:23.6648297Z         >>> input
2025-03-14T04:33:23.6648422Z         tensor([0, 1, 2, 3])     # Rank 0
2025-03-14T04:33:23.6648527Z         tensor([4, 5, 6, 7])     # Rank 1
2025-03-14T04:33:23.6648642Z         tensor([8, 9, 10, 11])   # Rank 2
2025-03-14T04:33:23.6648905Z         tensor([12, 13, 14, 15]) # Rank 3
2025-03-14T04:33:23.6649071Z         >>> output = torch.empty([4], dtype=torch.int64)
2025-03-14T04:33:23.6649201Z         >>> dist.all_to_all_single(output, input)
2025-03-14T04:33:23.6649308Z         >>> output
2025-03-14T04:33:23.6649412Z         tensor([0, 4, 8, 12])    # Rank 0
2025-03-14T04:33:23.6649531Z         tensor([1, 5, 9, 13])    # Rank 1
2025-03-14T04:33:23.6649635Z         tensor([2, 6, 10, 14])   # Rank 2
2025-03-14T04:33:23.6649753Z         tensor([3, 7, 11, 15])   # Rank 3
2025-03-14T04:33:23.6649841Z     
2025-03-14T04:33:23.6650025Z         >>> # Essentially, it is similar to following operation:
2025-03-14T04:33:23.6650172Z         >>> scatter_list = list(input.chunk(world_size))
2025-03-14T04:33:23.6650421Z         >>> gather_list = list(output.chunk(world_size))
2025-03-14T04:33:23.6650533Z         >>> for i in range(world_size):
2025-03-14T04:33:23.6650778Z         >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
2025-03-14T04:33:23.6650865Z     
2025-03-14T04:33:23.6651007Z         >>> # Another example with uneven split
2025-03-14T04:33:23.6651102Z         >>> input
2025-03-14T04:33:23.6651263Z         tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-14T04:33:23.6651441Z         tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-14T04:33:23.6651601Z         tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-14T04:33:23.6651778Z         tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-14T04:33:23.6651880Z         >>> input_splits
2025-03-14T04:33:23.6652023Z         [2, 2, 1, 1]                                                     # Rank 0
2025-03-14T04:33:23.6652151Z         [3, 2, 2, 2]                                                     # Rank 1
2025-03-14T04:33:23.6652290Z         [2, 1, 1, 1]                                                     # Rank 2
2025-03-14T04:33:23.6652427Z         [2, 2, 2, 1]                                                     # Rank 3
2025-03-14T04:33:23.6652567Z         >>> output_splits
2025-03-14T04:33:23.6652693Z         [2, 3, 2, 2]                                                     # Rank 0
2025-03-14T04:33:23.6652832Z         [2, 2, 1, 2]                                                     # Rank 1
2025-03-14T04:33:23.6652969Z         [1, 2, 1, 2]                                                     # Rank 2
2025-03-14T04:33:23.6653094Z         [1, 2, 1, 1]                                                     # Rank 3
2025-03-14T04:33:23.6653203Z         >>> output = ...
2025-03-14T04:33:23.6653419Z         >>> dist.all_to_all_single(output, input, output_splits, input_splits)
2025-03-14T04:33:23.6653508Z         >>> output
2025-03-14T04:33:23.6653685Z         tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
2025-03-14T04:33:23.6653842Z         tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
2025-03-14T04:33:23.6654013Z         tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
2025-03-14T04:33:23.6654186Z         tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
2025-03-14T04:33:23.6654272Z     
2025-03-14T04:33:23.6654356Z     
2025-03-14T04:33:23.6654538Z         >>> # Another example with tensors of torch.cfloat type.
2025-03-14T04:33:23.6654647Z         >>> input = torch.tensor(
2025-03-14T04:33:23.6654805Z         ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-14T04:33:23.6654908Z         ... ) + 4 * rank * (1 + 1j)
2025-03-14T04:33:23.6655077Z         >>> input
2025-03-14T04:33:23.6655258Z         tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
2025-03-14T04:33:23.6655449Z         tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
2025-03-14T04:33:23.6655636Z         tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
2025-03-14T04:33:23.6655836Z         tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
2025-03-14T04:33:23.6655985Z         >>> output = torch.empty([4], dtype=torch.int64)
2025-03-14T04:33:23.6656126Z         >>> dist.all_to_all_single(output, input)
2025-03-14T04:33:23.6656219Z         >>> output
2025-03-14T04:33:23.6656410Z         tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
2025-03-14T04:33:23.6656587Z         tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
2025-03-14T04:33:23.6656775Z         tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
2025-03-14T04:33:23.6656955Z         tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
2025-03-14T04:33:23.6657057Z     
2025-03-14T04:33:23.6657310Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6657434Z     
2025-03-14T04:33:23.6657540Z       warnings.warn(msg)
2025-03-14T04:33:23.6657635Z     
2025-03-14T04:33:23.6657849Z --- Parse Warning: 42 / 116 ---
2025-03-14T04:33:23.6658782Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4523.
2025-03-14T04:33:23.6659047Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6659148Z     
2025-03-14T04:33:23.6659515Z     Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
2025-03-14T04:33:23.6659616Z     
2025-03-14T04:33:23.6659738Z     Complex tensors are supported.
2025-03-14T04:33:23.6659835Z     
2025-03-14T04:33:23.6659925Z     Args:
2025-03-14T04:33:23.6660166Z         output_tensor_list (list[Tensor]): List of tensors to be gathered one
2025-03-14T04:33:23.6660262Z             per rank.
2025-03-14T04:33:23.6660506Z         input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
2025-03-14T04:33:23.6660767Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:23.6660914Z             the default process group will be used.
2025-03-14T04:33:23.6661118Z         async_op (bool, optional): Whether this op should be an async op.
2025-03-14T04:33:23.6661220Z     
2025-03-14T04:33:23.6661312Z     Returns:
2025-03-14T04:33:23.6661471Z         Async work handle, if async_op is set to True.
2025-03-14T04:33:23.6661625Z         None, if not async_op or if not part of the group.
2025-03-14T04:33:23.6661724Z     
2025-03-14T04:33:23.6661825Z     .. warning::
2025-03-14T04:33:23.6662002Z         `all_to_all` is experimental and subject to change.
2025-03-14T04:33:23.6662092Z     
2025-03-14T04:33:23.6662199Z     Examples:
2025-03-14T04:33:23.6662329Z         >>> # xdoctest: +SKIP("Undefined rank")
2025-03-14T04:33:23.6662466Z         >>> input = torch.arange(4) + rank * 4
2025-03-14T04:33:23.6662584Z         >>> input = list(input.chunk(4))
2025-03-14T04:33:23.6662694Z         >>> input
2025-03-14T04:33:23.6662877Z         [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
2025-03-14T04:33:23.6663049Z         [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
2025-03-14T04:33:23.6663235Z         [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
2025-03-14T04:33:23.6663421Z         [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
2025-03-14T04:33:23.6663609Z         >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-14T04:33:23.6663744Z         >>> dist.all_to_all(output, input)
2025-03-14T04:33:23.6663893Z         >>> output
2025-03-14T04:33:23.6664067Z         [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
2025-03-14T04:33:23.6664248Z         [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
2025-03-14T04:33:23.6664417Z         [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
2025-03-14T04:33:23.6664600Z         [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
2025-03-14T04:33:23.6664689Z     
2025-03-14T04:33:23.6664871Z         >>> # Essentially, it is similar to following operation:
2025-03-14T04:33:23.6664982Z         >>> scatter_list = input
2025-03-14T04:33:23.6665101Z         >>> gather_list = output
2025-03-14T04:33:23.6665218Z         >>> for i in range(world_size):
2025-03-14T04:33:23.6665456Z         >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
2025-03-14T04:33:23.6665541Z     
2025-03-14T04:33:23.6665646Z         >>> input
2025-03-14T04:33:23.6665807Z         tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-14T04:33:23.6665985Z         tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-14T04:33:23.6666172Z         tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-14T04:33:23.6666343Z         tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-14T04:33:23.6666441Z         >>> input_splits
2025-03-14T04:33:23.6666581Z         [2, 2, 1, 1]                                                     # Rank 0
2025-03-14T04:33:23.6666710Z         [3, 2, 2, 2]                                                     # Rank 1
2025-03-14T04:33:23.6666845Z         [2, 1, 1, 1]                                                     # Rank 2
2025-03-14T04:33:23.6666970Z         [2, 2, 2, 1]                                                     # Rank 3
2025-03-14T04:33:23.6667079Z         >>> output_splits
2025-03-14T04:33:23.6667205Z         [2, 3, 2, 2]                                                     # Rank 0
2025-03-14T04:33:23.6667341Z         [2, 2, 1, 2]                                                     # Rank 1
2025-03-14T04:33:23.6667469Z         [1, 2, 1, 2]                                                     # Rank 2
2025-03-14T04:33:23.6667604Z         [1, 2, 1, 1]                                                     # Rank 3
2025-03-14T04:33:23.6667738Z         >>> input = list(input.split(input_splits))
2025-03-14T04:33:23.6667838Z         >>> input
2025-03-14T04:33:23.6668070Z         [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
2025-03-14T04:33:23.6668287Z         [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
2025-03-14T04:33:23.6668492Z         [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
2025-03-14T04:33:23.6668708Z         [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
2025-03-14T04:33:23.6668810Z         >>> output = ...
2025-03-14T04:33:23.6668944Z         >>> dist.all_to_all(output, input)
2025-03-14T04:33:23.6669034Z         >>> output
2025-03-14T04:33:23.6669252Z         [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
2025-03-14T04:33:23.6669454Z         [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
2025-03-14T04:33:23.6669673Z         [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
2025-03-14T04:33:23.6669882Z         [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
2025-03-14T04:33:23.6669983Z     
2025-03-14T04:33:23.6670151Z         >>> # Another example with tensors of torch.cfloat type.
2025-03-14T04:33:23.6670275Z         >>> input = torch.tensor(
2025-03-14T04:33:23.6670423Z         ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-14T04:33:23.6670541Z         ... ) + 4 * rank * (1 + 1j)
2025-03-14T04:33:23.6670706Z         >>> input = list(input.chunk(4))
2025-03-14T04:33:23.6670813Z         >>> input
2025-03-14T04:33:23.6671031Z         [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
2025-03-14T04:33:23.6671256Z         [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
2025-03-14T04:33:23.6671476Z         [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
2025-03-14T04:33:23.6671710Z         [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
2025-03-14T04:33:23.6671897Z         >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-14T04:33:23.6672030Z         >>> dist.all_to_all(output, input)
2025-03-14T04:33:23.6672123Z         >>> output
2025-03-14T04:33:23.6672348Z         [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
2025-03-14T04:33:23.6672561Z         [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
2025-03-14T04:33:23.6672792Z         [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
2025-03-14T04:33:23.6673031Z         [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
2025-03-14T04:33:23.6673129Z     
2025-03-14T04:33:23.6673215Z     
2025-03-14T04:33:23.6673485Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6673572Z     
2025-03-14T04:33:23.6673690Z       warnings.warn(msg)
2025-03-14T04:33:23.6673777Z     
2025-03-14T04:33:23.6674001Z --- Parse Warning: 43 / 116 ---
2025-03-14T04:33:23.6674844Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2.
2025-03-14T04:33:23.6675124Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6675215Z     
2025-03-14T04:33:23.6675359Z     Module ``torch.distributed.launch``.
2025-03-14T04:33:23.6675451Z     
2025-03-14T04:33:23.6675718Z     ``torch.distributed.launch`` is a module that spawns up multiple distributed
2025-03-14T04:33:23.6675880Z     training processes on each of the training nodes.
2025-03-14T04:33:23.6675982Z     
2025-03-14T04:33:23.6676078Z     .. warning::
2025-03-14T04:33:23.6676176Z     
2025-03-14T04:33:23.6676457Z         This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2025-03-14T04:33:23.6676558Z     
2025-03-14T04:33:23.6676800Z     The utility can be used for single-node distributed training, in which one or
2025-03-14T04:33:23.6677049Z     more processes per node will be spawned. The utility can be used for either
2025-03-14T04:33:23.6677265Z     CPU training or GPU training. If the utility is used for GPU training,
2025-03-14T04:33:23.6677530Z     each distributed process will be operating on a single GPU. This can achieve
2025-03-14T04:33:23.6677771Z     well-improved single-node training performance. It can also be used in
2025-03-14T04:33:23.6678054Z     multi-node distributed training, by spawning up multiple processes on each node
2025-03-14T04:33:23.6678290Z     for well-improved multi-node distributed training performance as well.
2025-03-14T04:33:23.6678535Z     This will especially be beneficial for systems with multiple Infiniband
2025-03-14T04:33:23.6678790Z     interfaces that have direct-GPU support, since all of them can be utilized for
2025-03-14T04:33:23.6678927Z     aggregated communication bandwidth.
2025-03-14T04:33:23.6679017Z     
2025-03-14T04:33:23.6679372Z     In both cases of single-node distributed training or multi-node distributed
2025-03-14T04:33:23.6679615Z     training, this utility will launch the given number of processes per node
2025-03-14T04:33:23.6679863Z     (``--nproc-per-node``). If used for GPU training, this number needs to be less
2025-03-14T04:33:23.6680119Z     or equal to the number of GPUs on the current system (``nproc_per_node``),
2025-03-14T04:33:23.6680375Z     and each process will be operating on a single GPU from *GPU 0 to
2025-03-14T04:33:23.6680489Z     GPU (nproc_per_node - 1)*.
2025-03-14T04:33:23.6680588Z     
2025-03-14T04:33:23.6680696Z     **How to use this module:**
2025-03-14T04:33:23.6680794Z     
2025-03-14T04:33:23.6680952Z     1. Single-Node multi-process distributed training
2025-03-14T04:33:23.6681055Z     
2025-03-14T04:33:23.6681148Z     ::
2025-03-14T04:33:23.6681237Z     
2025-03-14T04:33:23.6681491Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-14T04:33:23.6681684Z                    YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2025-03-14T04:33:23.6681825Z                    arguments of your training script)
2025-03-14T04:33:23.6681910Z     
2025-03-14T04:33:23.6682134Z     2. Multi-Node multi-process distributed training: (e.g. two nodes)
2025-03-14T04:33:23.6682219Z     
2025-03-14T04:33:23.6682315Z     
2025-03-14T04:33:23.6682469Z     Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2025-03-14T04:33:23.6682565Z     
2025-03-14T04:33:23.6682652Z     ::
2025-03-14T04:33:23.6682782Z     
2025-03-14T04:33:23.6683018Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-14T04:33:23.6683195Z                    --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2025-03-14T04:33:23.6683407Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-14T04:33:23.6683580Z                    and all other arguments of your training script)
2025-03-14T04:33:23.6683666Z     
2025-03-14T04:33:23.6683768Z     Node 2:
2025-03-14T04:33:23.6683853Z     
2025-03-14T04:33:23.6683956Z     ::
2025-03-14T04:33:23.6684042Z     
2025-03-14T04:33:23.6684295Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-14T04:33:23.6684456Z                    --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2025-03-14T04:33:23.6684682Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-14T04:33:23.6684840Z                    and all other arguments of your training script)
2025-03-14T04:33:23.6684942Z     
2025-03-14T04:33:23.6685110Z     3. To look up what optional arguments this module offers:
2025-03-14T04:33:23.6685209Z     
2025-03-14T04:33:23.6685296Z     ::
2025-03-14T04:33:23.6685396Z     
2025-03-14T04:33:23.6685539Z         python -m torch.distributed.launch --help
2025-03-14T04:33:23.6685649Z     
2025-03-14T04:33:23.6685749Z     
2025-03-14T04:33:23.6685856Z     **Important Notices:**
2025-03-14T04:33:23.6685958Z     
2025-03-14T04:33:23.6686149Z     1. This utility and multi-process distributed (single-node or
2025-03-14T04:33:23.6686412Z     multi-node) GPU training currently only achieves the best performance using
2025-03-14T04:33:23.6686668Z     the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2025-03-14T04:33:23.6686785Z     use for GPU training.
2025-03-14T04:33:23.6686874Z     
2025-03-14T04:33:23.6687107Z     2. In your training program, you must parse the command-line argument:
2025-03-14T04:33:23.6687340Z     ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2025-03-14T04:33:23.6687587Z     If your training program uses GPUs, you should ensure that your code only
2025-03-14T04:33:23.6687780Z     runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2025-03-14T04:33:23.6687879Z     
2025-03-14T04:33:23.6687993Z     Parsing the local_rank argument
2025-03-14T04:33:23.6688098Z     
2025-03-14T04:33:23.6688188Z     ::
2025-03-14T04:33:23.6688290Z     
2025-03-14T04:33:23.6688396Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6688516Z         >>> import argparse
2025-03-14T04:33:23.6688651Z         >>> parser = argparse.ArgumentParser()
2025-03-14T04:33:23.6688867Z         >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2025-03-14T04:33:23.6688985Z         >>> args = parser.parse_args()
2025-03-14T04:33:23.6689112Z     
2025-03-14T04:33:23.6689271Z     Set your device to local rank using either
2025-03-14T04:33:23.6689378Z     
2025-03-14T04:33:23.6689467Z     ::
2025-03-14T04:33:23.6689573Z     
2025-03-14T04:33:23.6689780Z         >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2025-03-14T04:33:23.6689883Z     
2025-03-14T04:33:23.6689974Z     or
2025-03-14T04:33:23.6690062Z     
2025-03-14T04:33:23.6690168Z     ::
2025-03-14T04:33:23.6690256Z     
2025-03-14T04:33:23.6690416Z         >>> with torch.cuda.device(args.local_rank):
2025-03-14T04:33:23.6690524Z         >>>    # your code to run
2025-03-14T04:33:23.6690632Z         >>>    ...
2025-03-14T04:33:23.6690723Z     
2025-03-14T04:33:23.6690851Z     .. versionchanged:: 2.0.0
2025-03-14T04:33:23.6690941Z     
2025-03-14T04:33:23.6691208Z         The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2025-03-14T04:33:23.6691455Z         From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2025-03-14T04:33:23.6691623Z         previously used underscored ``--local_rank``.
2025-03-14T04:33:23.6691712Z     
2025-03-14T04:33:23.6691967Z         For backward compatibility, it may be necessary for users to handle both
2025-03-14T04:33:23.6692263Z         cases in their argument parsing code. This means including both ``"--local-rank"``
2025-03-14T04:33:23.6692497Z         and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2025-03-14T04:33:23.6692753Z         provided, the launcher will trigger an error: "error: unrecognized arguments:
2025-03-14T04:33:23.6693001Z         --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2025-03-14T04:33:23.6693161Z         including ``"--local-rank"`` should be sufficient.
2025-03-14T04:33:23.6693265Z     
2025-03-14T04:33:23.6693502Z     3. In your training program, you are supposed to call the following function
2025-03-14T04:33:23.6693759Z     at the beginning to start the distributed backend. It is strongly recommended
2025-03-14T04:33:23.6693989Z     that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2025-03-14T04:33:23.6694201Z     but ``env://`` is the one that is officially supported by this module.
2025-03-14T04:33:23.6694290Z     
2025-03-14T04:33:23.6694391Z     ::
2025-03-14T04:33:23.6694479Z     
2025-03-14T04:33:23.6694697Z         >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2025-03-14T04:33:23.6694866Z         >>>                                      init_method='env://')
2025-03-14T04:33:23.6694966Z     
2025-03-14T04:33:23.6695208Z     4. In your training program, you can either use regular distributed functions
2025-03-14T04:33:23.6695458Z     or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2025-03-14T04:33:23.6695669Z     training program uses GPUs for training and you would like to use
2025-03-14T04:33:23.6695877Z     :func:`torch.nn.parallel.DistributedDataParallel` module,
2025-03-14T04:33:23.6695989Z     here is how to configure it.
2025-03-14T04:33:23.6696096Z     
2025-03-14T04:33:23.6696185Z     ::
2025-03-14T04:33:23.6696283Z     
2025-03-14T04:33:23.6696480Z         >>> model = torch.nn.parallel.DistributedDataParallel(model,
2025-03-14T04:33:23.6696641Z         >>>                                                   device_ids=[args.local_rank],
2025-03-14T04:33:23.6696788Z         >>>                                                   output_device=args.local_rank)
2025-03-14T04:33:23.6696887Z     
2025-03-14T04:33:23.6697135Z     Please ensure that ``device_ids`` argument is set to be the only GPU device id
2025-03-14T04:33:23.6697382Z     that your code will be operating on. This is generally the local rank of the
2025-03-14T04:33:23.6697625Z     process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2025-03-14T04:33:23.6697858Z     and ``output_device`` needs to be ``args.local_rank`` in order to use this
2025-03-14T04:33:23.6697950Z     utility
2025-03-14T04:33:23.6698050Z     
2025-03-14T04:33:23.6698349Z     5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2025-03-14T04:33:23.6698586Z     ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2025-03-14T04:33:23.6698813Z     ``--use-env=True``. You must adjust the subprocess example above to replace
2025-03-14T04:33:23.6699031Z     ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2025-03-14T04:33:23.6699211Z     will not pass ``--local-rank`` when you specify this flag.
2025-03-14T04:33:23.6699313Z     
2025-03-14T04:33:23.6699411Z     .. warning::
2025-03-14T04:33:23.6699497Z     
2025-03-14T04:33:23.6699718Z         ``local_rank`` is NOT globally unique: it is only unique per process
2025-03-14T04:33:23.6699909Z         on a machine.  Thus, don't use it to decide if you should, e.g.,
2025-03-14T04:33:23.6700052Z         write to a networked filesystem.  See
2025-03-14T04:33:23.6700274Z         https://github.com/pytorch/pytorch/issues/12042 for an example of
2025-03-14T04:33:23.6700463Z         how things can go wrong if you don't do this correctly.
2025-03-14T04:33:23.6700552Z     
2025-03-14T04:33:23.6700650Z     
2025-03-14T04:33:23.6700764Z     
2025-03-14T04:33:23.6700865Z     
2025-03-14T04:33:23.6701121Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6701221Z     
2025-03-14T04:33:23.6701329Z       warnings.warn(msg)
2025-03-14T04:33:23.6701426Z     
2025-03-14T04:33:23.6701658Z --- Parse Warning: 44 / 116 ---
2025-03-14T04:33:23.6702712Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361.
2025-03-14T04:33:23.6702979Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6703083Z     
2025-03-14T04:33:23.6703331Z     Creates an :class:`ShardedTensor` from local shards and the global metadata.
2025-03-14T04:33:23.6703504Z     Needs to be called on all ranks in an SPMD fashion.
2025-03-14T04:33:23.6703593Z     
2025-03-14T04:33:23.6703699Z     Args:
2025-03-14T04:33:23.6703975Z         local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2025-03-14T04:33:23.6704164Z             of shards that represent the local shards on this rank.
2025-03-14T04:33:23.6704418Z         global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2025-03-14T04:33:23.6704558Z             shape of the overall sharded tensor.
2025-03-14T04:33:23.6704644Z     
2025-03-14T04:33:23.6704753Z     Keyword args:
2025-03-14T04:33:23.6705017Z         process_group (ProcessGroup, optional): The process group to work on. If None,
2025-03-14T04:33:23.6705161Z             the default process group will be used.
2025-03-14T04:33:23.6705339Z         init_rrefs (bool, optional): Whether or not to initialize
2025-03-14T04:33:23.6705566Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-14T04:33:23.6705762Z             Need to initialize the RPC Framework if specified as ``True``.
2025-03-14T04:33:23.6705881Z             Default: ``False``.
2025-03-14T04:33:23.6705967Z     
2025-03-14T04:33:23.6706072Z     Returns:
2025-03-14T04:33:23.6706230Z         A :class:`ShardedTensor` object handle on this rank
2025-03-14T04:33:23.6706330Z     
2025-03-14T04:33:23.6706416Z     
2025-03-14T04:33:23.6706520Z     Examples:
2025-03-14T04:33:23.6706775Z         Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2025-03-14T04:33:23.6706976Z         each shard have a (5, 5) local tensor, we can do it like below:
2025-03-14T04:33:23.6707061Z     
2025-03-14T04:33:23.6707166Z         on rank 0:
2025-03-14T04:33:23.6707292Z         >>> # xdoctest: +SKIP("not distributed")
2025-03-14T04:33:23.6707422Z         >>> local_shard_metadata = ShardMetadata(
2025-03-14T04:33:23.6707546Z         >>>     shard_offsets=[0, 0],
2025-03-14T04:33:23.6707704Z         >>>     shard_lengths=[5, 5],
2025-03-14T04:33:23.6707835Z         >>>     placement="rank:0/cuda:0"
2025-03-14T04:33:23.6707928Z         >>> )
2025-03-14T04:33:23.6708140Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-14T04:33:23.6708338Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-14T04:33:23.6708439Z     
2025-03-14T04:33:23.6708530Z         on rank 1:
2025-03-14T04:33:23.6708668Z         >>> # xdoctest: +SKIP("not distributed")
2025-03-14T04:33:23.6708795Z         >>> local_shard_metadata = ShardMetadata(
2025-03-14T04:33:23.6708915Z         >>>     shard_offsets=[5, 0],
2025-03-14T04:33:23.6709022Z         >>>     shard_lengths=[5, 5],
2025-03-14T04:33:23.6709148Z         >>>     placement="rank:1/cuda:1"
2025-03-14T04:33:23.6709237Z         >>> )
2025-03-14T04:33:23.6709446Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-14T04:33:23.6709644Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-14T04:33:23.6709743Z     
2025-03-14T04:33:23.6710000Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6710125Z     
2025-03-14T04:33:23.6710229Z       warnings.warn(msg)
2025-03-14T04:33:23.6710327Z     
2025-03-14T04:33:23.6710526Z --- Parse Warning: 45 / 116 ---
2025-03-14T04:33:23.6711612Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=799.
2025-03-14T04:33:23.6711876Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6711975Z     
2025-03-14T04:33:23.6712237Z     Initialize a ShardedTensor given only one local tensor, global sharded tensor
2025-03-14T04:33:23.6712373Z     size and sharding spec on each rank.
2025-03-14T04:33:23.6712462Z     
2025-03-14T04:33:23.6712565Z     Args:
2025-03-14T04:33:23.6712795Z         local_tensor (Tensor): Single tensor of local shard stored in each rank.
2025-03-14T04:33:23.6713079Z         sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2025-03-14T04:33:23.6713255Z             The specification describing how to shard the Tensor.
2025-03-14T04:33:23.6713445Z         global_size (Sequence[int]): Size of the sharded tensor.
2025-03-14T04:33:23.6713724Z         process_group (ProcessGroup, optional): The process group to aggregate on.
2025-03-14T04:33:23.6713840Z             Default: None
2025-03-14T04:33:23.6714019Z         init_rrefs (bool, optional): Whether or not to initialize
2025-03-14T04:33:23.6714246Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-14T04:33:23.6714444Z             Need to initialize the RPC Framework if specified as ``True``.
2025-03-14T04:33:23.6714564Z             Default: ``False``.
2025-03-14T04:33:23.6714656Z     
2025-03-14T04:33:23.6714762Z     Returns:
2025-03-14T04:33:23.6715007Z         A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2025-03-14T04:33:23.6715148Z             tensor stored in the current rank.
2025-03-14T04:33:23.6715237Z     
2025-03-14T04:33:23.6715339Z     Examples:
2025-03-14T04:33:23.6715445Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6715601Z         >>> # All tensors below are of torch.int64 type.
2025-03-14T04:33:23.6715728Z         >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:23.6715922Z         >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2025-03-14T04:33:23.6716126Z         >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2025-03-14T04:33:23.6716225Z         >>> local_tensor
2025-03-14T04:33:23.6716347Z         tensor([[1, 2, 3, 4]]) # Rank 0
2025-03-14T04:33:23.6716452Z         tensor([[3, 4, 5, 6]]) # Rank 1
2025-03-14T04:33:23.6716567Z         >>> sharding_dim = 0
2025-03-14T04:33:23.6716765Z         >>> sharding_spec = ChunkShardingSpec(
2025-03-14T04:33:23.6716886Z                 dim=sharding_dim,
2025-03-14T04:33:23.6716991Z                 placements=[
2025-03-14T04:33:23.6717111Z                     "rank:0/cuda:0",
2025-03-14T04:33:23.6717216Z                     "rank:1/cuda:1",
2025-03-14T04:33:23.6717322Z                 ],
2025-03-14T04:33:23.6717413Z             )
2025-03-14T04:33:23.6717573Z         >>> st = ShardedTensor._init_from_local_tensor(
2025-03-14T04:33:23.6717706Z         ...     local_tensor, sharding_spec, [2, 4]
2025-03-14T04:33:23.6717811Z         ... )
2025-03-14T04:33:23.6717904Z         >>> st
2025-03-14T04:33:23.6718021Z         ShardedTensor(
2025-03-14T04:33:23.6718137Z             ShardedTensorMetadata(
2025-03-14T04:33:23.6718259Z                 shards_metadata=[
2025-03-14T04:33:23.6718534Z                     ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2025-03-14T04:33:23.6718820Z                     ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2025-03-14T04:33:23.6718914Z                 ],
2025-03-14T04:33:23.6719067Z                 size=torch.Size([2, 4])
2025-03-14T04:33:23.6719241Z         )
2025-03-14T04:33:23.6719364Z         >>> st.local_tensor()
2025-03-14T04:33:23.6719473Z         tensor([1, 2, 3, 4]) # Rank 0
2025-03-14T04:33:23.6719597Z         tensor([3, 4, 5, 6]) # Rank 1
2025-03-14T04:33:23.6719686Z     
2025-03-14T04:33:23.6719980Z     Warning: This API is experimental and subject to change. It lacks of a fully across
2025-03-14T04:33:23.6720226Z              rank validations, and we only validate the local shard on the current rank.
2025-03-14T04:33:23.6720460Z              We fully rely on the user to ensure local tensor is sharded based on the
2025-03-14T04:33:23.6720565Z              sharding spec.
2025-03-14T04:33:23.6720668Z     
2025-03-14T04:33:23.6720928Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6721035Z     
2025-03-14T04:33:23.6721141Z       warnings.warn(msg)
2025-03-14T04:33:23.6721229Z     
2025-03-14T04:33:23.6721456Z --- Parse Warning: 46 / 116 ---
2025-03-14T04:33:23.6722508Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1040.
2025-03-14T04:33:23.6722806Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6722908Z     
2025-03-14T04:33:23.6723164Z     Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2025-03-14T04:33:23.6723282Z     single local shard.
2025-03-14T04:33:23.6723367Z     
2025-03-14T04:33:23.6723596Z     If ``resharding_spec`` is same as the original one, this becomes a no-op.
2025-03-14T04:33:23.6723857Z     If only ``resharding_spec`` shares the same sharding dim with the original one,
2025-03-14T04:33:23.6723972Z     we swap local shards directly.
2025-03-14T04:33:23.6724252Z     For more generic cases, we merge different shards across different ranks and split
2025-03-14T04:33:23.6724506Z     the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2025-03-14T04:33:23.6724608Z     
2025-03-14T04:33:23.6724696Z     Args:
2025-03-14T04:33:23.6725003Z         resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2025-03-14T04:33:23.6725174Z             specification describing how the tensor is sharded.
2025-03-14T04:33:23.6725273Z     
2025-03-14T04:33:23.6725362Z     Returns:
2025-03-14T04:33:23.6725579Z         A :class:`ShardedTensor` object whose local shards are resharded.
2025-03-14T04:33:23.6725665Z     
2025-03-14T04:33:23.6725768Z     Examples:
2025-03-14T04:33:23.6725872Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6726009Z         >>> # We have 2 process groups, 2 ranks.
2025-03-14T04:33:23.6726241Z         >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2025-03-14T04:33:23.6726383Z         >>> tensor = torch.stack([tensor, tensor])
2025-03-14T04:33:23.6726475Z         >>> tensor
2025-03-14T04:33:23.6726614Z         tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2025-03-14T04:33:23.6726738Z         tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2025-03-14T04:33:23.6726872Z         tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2025-03-14T04:33:23.6727000Z         tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2025-03-14T04:33:23.6727119Z         >>> sharding_dim = 0
2025-03-14T04:33:23.6727237Z         >>> spec = ChunkShardingSpec(
2025-03-14T04:33:23.6727355Z                 dim=sharding_dim,
2025-03-14T04:33:23.6727457Z                 placements=[
2025-03-14T04:33:23.6727573Z                     "rank:0/cuda:0",
2025-03-14T04:33:23.6727676Z                     "rank:1/cuda:1",
2025-03-14T04:33:23.6727789Z                     "rank:2/cuda:2",
2025-03-14T04:33:23.6727889Z                     "rank:3/cuda:3",
2025-03-14T04:33:23.6727992Z                 ],
2025-03-14T04:33:23.6728083Z             )
2025-03-14T04:33:23.6728206Z         >>> current_offsets = [0] * 2
2025-03-14T04:33:23.6728347Z         >>> current_offsets[0] = rank * 2
2025-03-14T04:33:23.6728480Z         >>> shard_metadata = ShardMetadata(
2025-03-14T04:33:23.6728636Z                 shard_offsets=copy.deepcopy(current_offsets),
2025-03-14T04:33:23.6728766Z                 shard_sizes=tensor.size(),
2025-03-14T04:33:23.6728898Z                 placement=spec.placements[rank],
2025-03-14T04:33:23.6728987Z             )
2025-03-14T04:33:23.6729103Z         >>> local_shards = [
2025-03-14T04:33:23.6729196Z                 Shard(
2025-03-14T04:33:23.6729314Z                     tensor=tensor,
2025-03-14T04:33:23.6729431Z                     metadata=shard_metadata,
2025-03-14T04:33:23.6729533Z                 )
2025-03-14T04:33:23.6729622Z             ]
2025-03-14T04:33:23.6729872Z         >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2025-03-14T04:33:23.6729978Z         >>> sharding_dim = 1
2025-03-14T04:33:23.6730124Z         >>> resharding_spec = ChunkShardingSpec(
2025-03-14T04:33:23.6730230Z                 dim=sharding_dim,
2025-03-14T04:33:23.6730346Z                 placements=[
2025-03-14T04:33:23.6730448Z                     "rank:0/cuda:0",
2025-03-14T04:33:23.6730566Z                     "rank:1/cuda:1",
2025-03-14T04:33:23.6730666Z                     "rank:2/cuda:2",
2025-03-14T04:33:23.6730822Z                     "rank:3/cuda:3",
2025-03-14T04:33:23.6730913Z                 ],
2025-03-14T04:33:23.6731014Z             )
2025-03-14T04:33:23.6731130Z         >>> st.reshard(resharding_spec)
2025-03-14T04:33:23.6731267Z         >>> tensor = st.local_shards()[0].tensor
2025-03-14T04:33:23.6731359Z         >>> tensor
2025-03-14T04:33:23.6731521Z         tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2025-03-14T04:33:23.6731666Z         tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2025-03-14T04:33:23.6731827Z         tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2025-03-14T04:33:23.6731973Z         tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2025-03-14T04:33:23.6732077Z     
2025-03-14T04:33:23.6732334Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6732432Z     
2025-03-14T04:33:23.6732534Z       warnings.warn(msg)
2025-03-14T04:33:23.6732634Z     
2025-03-14T04:33:23.6732842Z --- Parse Warning: 47 / 116 ---
2025-03-14T04:33:23.6733831Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12.
2025-03-14T04:33:23.6734094Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6734196Z     
2025-03-14T04:33:23.6734451Z     Representation of a sharding plan, describes how to shard a module
2025-03-14T04:33:23.6734765Z     across hosts. `plan` is used to shard module parameters according to the spec provided,
2025-03-14T04:33:23.6735055Z     `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2025-03-14T04:33:23.6735317Z     layout of a module with a spec, and when to convert back to data parallel fashion.
2025-03-14T04:33:23.6735402Z     
2025-03-14T04:33:23.6735503Z     Args:
2025-03-14T04:33:23.6735775Z         plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2025-03-14T04:33:23.6735963Z               :class:`torch.distributed._shard.sharder.Sharder`]):
2025-03-14T04:33:23.6736239Z             a dict describes how to shard a module, there're currently two ways to shard a module:
2025-03-14T04:33:23.6736506Z                 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2025-03-14T04:33:23.6736634Z                    a parameter to a `ShardingSpec`.
2025-03-14T04:33:23.6736912Z                 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2025-03-14T04:33:23.6737050Z                    to a `Sharder` object.
2025-03-14T04:33:23.6737395Z         output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2025-03-14T04:33:23.6737655Z             a dict specifies the layout of a module's output which produces a ShardedTensor,
2025-03-14T04:33:23.6737906Z             keyed by the name of module to ShardingSpec("" in key means the root module).
2025-03-14T04:33:23.6738008Z             Default: `None`
2025-03-14T04:33:23.6738276Z         return_local_tensor (List[str], optional): a list of string, each element enables
2025-03-14T04:33:23.6738519Z             a module's sharded output to be returned as a Tensor from its local shards to
2025-03-14T04:33:23.6738784Z             ensure further processing in a data parallel fashion. ("" in list means the
2025-03-14T04:33:23.6738888Z             root module).
2025-03-14T04:33:23.6739000Z             Default: None
2025-03-14T04:33:23.6739091Z     Example:
2025-03-14T04:33:23.6739391Z       Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2025-03-14T04:33:23.6739676Z       want to convert the output of the second linear layer back to DDP, we can do it as follows:
2025-03-14T04:33:23.6739775Z     
2025-03-14T04:33:23.6739974Z         >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2025-03-14T04:33:23.6740102Z         >>> class MyModule(nn.Module):
2025-03-14T04:33:23.6740218Z         >>>     def __init__(self) -> None:
2025-03-14T04:33:23.6740337Z         >>>        super().__init__()
2025-03-14T04:33:23.6740448Z         >>>        self.fc1 = nn.Linear()
2025-03-14T04:33:23.6740570Z         >>>        self.gelu = nn.GELU()
2025-03-14T04:33:23.6740680Z         >>>        self.fc2 = nn.Linear()
2025-03-14T04:33:23.6740790Z         >>>        self.relu = nn.Linear()
2025-03-14T04:33:23.6740896Z         >>>
2025-03-14T04:33:23.6741007Z         >>>     def forward(self, input):
2025-03-14T04:33:23.6741203Z         >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2025-03-14T04:33:23.6741293Z     
2025-03-14T04:33:23.6741395Z     
2025-03-14T04:33:23.6741535Z         >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2025-03-14T04:33:23.6741669Z         >>> sharding_plan = ShardingPlan(
2025-03-14T04:33:23.6741766Z         >>>    plan={
2025-03-14T04:33:23.6741897Z         >>>        "fc1.weight": spec1,
2025-03-14T04:33:23.6742007Z         >>>        "fc2.weight": spec2
2025-03-14T04:33:23.6742111Z         >>>    },
2025-03-14T04:33:23.6742215Z         >>>    output_plan={
2025-03-14T04:33:23.6742339Z         >>>        "fc2": output_spec
2025-03-14T04:33:23.6742429Z         >>>    },
2025-03-14T04:33:23.6742557Z         >>>    return_local_tensor=["fc2"]
2025-03-14T04:33:23.6742646Z         >>> )
2025-03-14T04:33:23.6742744Z     
2025-03-14T04:33:23.6743027Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6743150Z     
2025-03-14T04:33:23.6743253Z       warnings.warn(msg)
2025-03-14T04:33:23.6743357Z     
2025-03-14T04:33:23.6743559Z --- Parse Warning: 48 / 116 ---
2025-03-14T04:33:23.6744668Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72.
2025-03-14T04:33:23.6744932Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6745031Z     
2025-03-14T04:33:23.6745153Z     Run post-localSGD algorithm.
2025-03-14T04:33:23.6745256Z     
2025-03-14T04:33:23.6745501Z     This DDP communication hook is used for running post-localSGD algorithm,
2025-03-14T04:33:23.6745684Z     by combining with a model averaging component (e.g.,
2025-03-14T04:33:23.6746018Z     :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2025-03-14T04:33:23.6746156Z     that runs after the optimizer step.
2025-03-14T04:33:23.6746270Z     
2025-03-14T04:33:23.6746374Z     Args:
2025-03-14T04:33:23.6746602Z         state (PostLocalSGDState): State information to run post-localSGD.
2025-03-14T04:33:23.6746894Z             Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2025-03-14T04:33:23.6747319Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-14T04:33:23.6747587Z             Note that since DDP comm hook only supports single process single device mode,
2025-03-14T04:33:23.6747749Z             only exactly one tensor is stored in this bucket.
2025-03-14T04:33:23.6747854Z     
2025-03-14T04:33:23.6747947Z     Returns:
2025-03-14T04:33:23.6748211Z         Future handler of the communication, which updates the gradients in place.
2025-03-14T04:33:23.6748305Z     
2025-03-14T04:33:23.6748405Z     Example::
2025-03-14T04:33:23.6748528Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6748946Z         >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2025-03-14T04:33:23.6749092Z                                   start_localSGD_iter=10)
2025-03-14T04:33:23.6749272Z         >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:23.6749708Z         >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2025-03-14T04:33:23.6750058Z         >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2025-03-14T04:33:23.6750163Z     
2025-03-14T04:33:23.6750422Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6750526Z     
2025-03-14T04:33:23.6750632Z       warnings.warn(msg)
2025-03-14T04:33:23.6750739Z     
2025-03-14T04:33:23.6750947Z --- Parse Warning: 49 / 116 ---
2025-03-14T04:33:23.6752020Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342.
2025-03-14T04:33:23.6752290Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6752395Z     
2025-03-14T04:33:23.6752515Z     Implement PowerSGD algorithm.
2025-03-14T04:33:23.6752619Z     
2025-03-14T04:33:23.6752850Z     This DDP communication hook implements PowerSGD gradient compression
2025-03-14T04:33:23.6753112Z     algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2025-03-14T04:33:23.6753360Z     Once gradient tensors are aggregated across all workers, this hook applies
2025-03-14T04:33:23.6753482Z     compression as follows:
2025-03-14T04:33:23.6753569Z     
2025-03-14T04:33:23.6754054Z     1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2025-03-14T04:33:23.6754175Z     
2025-03-14T04:33:23.6754609Z         1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2025-03-14T04:33:23.6754694Z     
2025-03-14T04:33:23.6755113Z         1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2025-03-14T04:33:23.6755201Z     
2025-03-14T04:33:23.6755332Z     2. Handles uncompressed tensors:
2025-03-14T04:33:23.6755418Z     
2025-03-14T04:33:23.6755947Z         2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2025-03-14T04:33:23.6756032Z     
2025-03-14T04:33:23.6756387Z         2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2025-03-14T04:33:23.6756474Z     
2025-03-14T04:33:23.6756730Z     3. Handles the tensors that should be compressed by PowerSGD compression:
2025-03-14T04:33:23.6756816Z     
2025-03-14T04:33:23.6757107Z         3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2025-03-14T04:33:23.6757419Z         such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2025-03-14T04:33:23.6757518Z     
2025-03-14T04:33:23.6757672Z         3.2. Computes each P in Ps, which is equal to MQ;
2025-03-14T04:33:23.6757771Z     
2025-03-14T04:33:23.6757886Z         3.3. Allreduces Ps as a batch;
2025-03-14T04:33:23.6757987Z     
2025-03-14T04:33:23.6758108Z         3.4. Orthogonalizes each P in Ps;
2025-03-14T04:33:23.6758207Z     
2025-03-14T04:33:23.6758407Z         3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2025-03-14T04:33:23.6758507Z     
2025-03-14T04:33:23.6758620Z         3.6. Allreduces Qs as a batch;
2025-03-14T04:33:23.6758724Z     
2025-03-14T04:33:23.6759029Z         3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2025-03-14T04:33:23.6759204Z     
2025-03-14T04:33:23.6759621Z     Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2025-03-14T04:33:23.6759921Z     This not only gives the user more control over the tradeoff between speedup and accuracy,
2025-03-14T04:33:23.6760388Z     but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2025-03-14T04:33:23.6760491Z     
2025-03-14T04:33:23.6760582Z     Args:
2025-03-14T04:33:23.6761028Z         state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2025-03-14T04:33:23.6761383Z             To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2025-03-14T04:33:23.6761515Z             and ``min_compression_rate``.
2025-03-14T04:33:23.6761933Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-14T04:33:23.6762198Z             Note that since DDP comm hook only supports single process single device mode,
2025-03-14T04:33:23.6762357Z             only exactly one tensor is stored in this bucket.
2025-03-14T04:33:23.6762461Z     
2025-03-14T04:33:23.6762553Z     Returns:
2025-03-14T04:33:23.6762814Z         Future handler of the communication, which updates the gradients in place.
2025-03-14T04:33:23.6762903Z     
2025-03-14T04:33:23.6763016Z     Example::
2025-03-14T04:33:23.6763123Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6763412Z         >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2025-03-14T04:33:23.6763581Z                                   start_powerSGD_iter=10, min_compression_rate=0.5)
2025-03-14T04:33:23.6763758Z         >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2025-03-14T04:33:23.6763874Z     
2025-03-14T04:33:23.6764171Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6764259Z     
2025-03-14T04:33:23.6764366Z       warnings.warn(msg)
2025-03-14T04:33:23.6764465Z     
2025-03-14T04:33:23.6764675Z --- Parse Warning: 50 / 116 ---
2025-03-14T04:33:23.6765789Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38.
2025-03-14T04:33:23.6766070Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6766158Z     
2025-03-14T04:33:23.6766353Z     Averages parameters periodically after the warm-up stage.
2025-03-14T04:33:23.6766457Z     
2025-03-14T04:33:23.6766718Z     This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-14T04:33:23.6766937Z     by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2025-03-14T04:33:23.6767176Z     using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2025-03-14T04:33:23.6767308Z     
2025-03-14T04:33:23.6767397Z     Args:
2025-03-14T04:33:23.6767582Z         period (int): The number of steps per model averaging.
2025-03-14T04:33:23.6767854Z                       Usually the period should be greater than ``1`` to reduce the communication cost.
2025-03-14T04:33:23.6768005Z                       Otherwise, only DDP needs to be used.
2025-03-14T04:33:23.6768215Z         warmup_steps (int): The number of warm-up steps. During this stage,
2025-03-14T04:33:23.6768355Z                             model averaging is skipped.
2025-03-14T04:33:23.6768547Z         process_group: The process group to be used for all-reduce.
2025-03-14T04:33:23.6768707Z                        If ``None``, the default process group, which
2025-03-14T04:33:23.6768903Z                        is created by :func:`torch.distributed.init_process_group`,
2025-03-14T04:33:23.6769045Z                        will be used. (default: ``None``)
2025-03-14T04:33:23.6769132Z     
2025-03-14T04:33:23.6769240Z     Example::
2025-03-14T04:33:23.6769325Z     
2025-03-14T04:33:23.6769471Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6769570Z         >>> import torch
2025-03-14T04:33:23.6769711Z         >>> import torch.distributed as dist
2025-03-14T04:33:23.6770057Z         >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2025-03-14T04:33:23.6770348Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-14T04:33:23.6770458Z         >>> import torch.nn as nn
2025-03-14T04:33:23.6770559Z         >>>
2025-03-14T04:33:23.6770739Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-14T04:33:23.6770873Z         >>> torch.cuda.set_device(rank)
2025-03-14T04:33:23.6771011Z         >>> module = nn.Linear(1, 1, bias=False).cuda()
2025-03-14T04:33:23.6771186Z         >>> model = nn.parallel.DistributedDataParallel(
2025-03-14T04:33:23.6771331Z         >>>    module, device_ids=[rank], output_device=rank
2025-03-14T04:33:23.6771434Z         >>> )
2025-03-14T04:33:23.6771586Z         >>> # Register a post-localSGD communication hook.
2025-03-14T04:33:23.6771900Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-14T04:33:23.6772069Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:23.6772173Z         >>>
2025-03-14T04:33:23.6772439Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-14T04:33:23.6772615Z         >>> # After 100 steps, run model averaging every 4 steps.
2025-03-14T04:33:23.6772933Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-14T04:33:23.6773285Z         >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-14T04:33:23.6773422Z         >>> for step in range(0, 200):
2025-03-14T04:33:23.6773549Z         >>>    optimizer.zero_grad()
2025-03-14T04:33:23.6773670Z         >>>    loss = loss_fn(output, labels)
2025-03-14T04:33:23.6773793Z         >>>    loss.backward()
2025-03-14T04:33:23.6773900Z         >>>    optimizer.step()
2025-03-14T04:33:23.6774113Z         >>>    # Will average model parameters globally every 4 steps. Thus,
2025-03-14T04:33:23.6774321Z         >>>    # inter-node communication only occurs every 4 iterations after
2025-03-14T04:33:23.6774466Z         >>>    # the initial ``warmup_steps`` period.
2025-03-14T04:33:23.6774633Z         >>>    averager.average_parameters(model.parameters())
2025-03-14T04:33:23.6774734Z     
2025-03-14T04:33:23.6774990Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6775094Z     
2025-03-14T04:33:23.6775197Z       warnings.warn(msg)
2025-03-14T04:33:23.6775301Z     
2025-03-14T04:33:23.6775506Z --- Parse Warning: 51 / 116 ---
2025-03-14T04:33:23.6776723Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19.
2025-03-14T04:33:23.6777019Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6777124Z     
2025-03-14T04:33:23.6777459Z     Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2025-03-14T04:33:23.6777561Z     
2025-03-14T04:33:23.6777873Z     Process groups of different sizes are organized in a hierarchy, and they average parameters
2025-03-14T04:33:23.6778097Z     by using different periods concurrently after the warm-up stage.
2025-03-14T04:33:23.6778508Z     This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2025-03-14T04:33:23.6778857Z     that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2025-03-14T04:33:23.6798769Z     a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2025-03-14T04:33:23.6799332Z     level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2025-03-14T04:33:23.6799744Z     Similarly, the process groups within this class do not have such an intra-machine process
2025-03-14T04:33:23.6800042Z     subgroup, which should be embedded by the post-local SGD communication hook instead.
2025-03-14T04:33:23.6800133Z     
2025-03-14T04:33:23.6800241Z     Args:
2025-03-14T04:33:23.6800509Z         period_group_size_dict: An ordered dict mapping keys of model averaging period to
2025-03-14T04:33:23.6800731Z                                 process group size, used for initializing process groups of
2025-03-14T04:33:23.6800981Z                                 different sizes in a hierarchy to average parameters concurrently.
2025-03-14T04:33:23.6801199Z                                 Particularly, at each iteration, there will be at most a single
2025-03-14T04:33:23.6801456Z                                 process group that runs averaging -- the period of such group should
2025-03-14T04:33:23.6801679Z                                 have the largest period which the current step can be divided by.
2025-03-14T04:33:23.6801867Z                                 For example, if the dict has three keys: 2, 4, and 8,
2025-03-14T04:33:23.6802080Z                                 then this means totally three process groups will be created to
2025-03-14T04:33:23.6802309Z                                 average parameters every 2, 4, and 8 iterations, respectively.
2025-03-14T04:33:23.6802506Z                                 At the 4th iteration, only the second process group will run
2025-03-14T04:33:23.6802734Z                                 averaging, because the first process group should be a
2025-03-14T04:33:23.6802989Z                                 subset of the second process group, and no need to execute the first
2025-03-14T04:33:23.6803139Z                                 process group redundantly.
2025-03-14T04:33:23.6803343Z                                 On the other hand, the third process group can only be triggered
2025-03-14T04:33:23.6803591Z                                 every 8 iterations, so it will not be triggered at the 4th iteration.
2025-03-14T04:33:23.6803912Z         warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2025-03-14T04:33:23.6804357Z         process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2025-03-14T04:33:23.6804555Z                                                 If ``None``, the default process group, which is created
2025-03-14T04:33:23.6804772Z                                                 by :func:`torch.distributed.init_process_group`, will be used.
2025-03-14T04:33:23.6804918Z                                                 (default: ``None``)
2025-03-14T04:33:23.6805036Z     
2025-03-14T04:33:23.6805155Z     Example::
2025-03-14T04:33:23.6805281Z         >>> # xdoctest: +SKIP('undefined rank')
2025-03-14T04:33:23.6805425Z         >>> from collections import OrderedDict
2025-03-14T04:33:23.6805525Z         >>> import torch
2025-03-14T04:33:23.6805652Z         >>> import torch.distributed as dist
2025-03-14T04:33:23.6805946Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-14T04:33:23.6806058Z         >>>     PostLocalSGDState,
2025-03-14T04:33:23.6806180Z         >>>     post_localSGD_hook,
2025-03-14T04:33:23.6806271Z         >>> )
2025-03-14T04:33:23.6806664Z         >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2025-03-14T04:33:23.6806772Z         >>> import torch.nn as nn
2025-03-14T04:33:23.6806880Z         >>>
2025-03-14T04:33:23.6807065Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-14T04:33:23.6807199Z         >>> torch.cuda.set_device(rank)
2025-03-14T04:33:23.6807342Z         >>> module = nn.Linear(1, 1, bias=False).to(rank)
2025-03-14T04:33:23.6807515Z         >>> model = nn.parallel.DistributedDataParallel(
2025-03-14T04:33:23.6807665Z         >>>    module, device_ids=[rank], output_device=rank
2025-03-14T04:33:23.6807795Z         >>> )
2025-03-14T04:33:23.6807949Z         >>> # Register a post-localSGD communication hook.
2025-03-14T04:33:23.6808241Z         >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2025-03-14T04:33:23.6808369Z         >>> subgroup, _ = dist.new_subgroups()
2025-03-14T04:33:23.6808698Z         >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2025-03-14T04:33:23.6808867Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:23.6808974Z         >>>
2025-03-14T04:33:23.6809258Z         >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2025-03-14T04:33:23.6809403Z         >>> # the 16 processes every 16 iterations.
2025-03-14T04:33:23.6809593Z         >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2025-03-14T04:33:23.6809847Z         >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2025-03-14T04:33:23.6810167Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-14T04:33:23.6810449Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-14T04:33:23.6810610Z         >>> # After 100 steps, run model averaging at two levels.
2025-03-14T04:33:23.6810759Z         >>> for step in range(0, 200):
2025-03-14T04:33:23.6810871Z         >>>    optimizer.zero_grad()
2025-03-14T04:33:23.6811030Z         >>>    loss = loss_fn(output, labels)
2025-03-14T04:33:23.6811162Z         >>>    loss.backward()
2025-03-14T04:33:23.6811283Z         >>>    optimizer.step()
2025-03-14T04:33:23.6811447Z         >>>    # Average parameters after ``optimizer.step()``.
2025-03-14T04:33:23.6811748Z         >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2025-03-14T04:33:23.6811915Z         >>>    averager.average_parameters(model.parameters())
2025-03-14T04:33:23.6812016Z     
2025-03-14T04:33:23.6812115Z     .. warning ::
2025-03-14T04:33:23.6812382Z         The last group size in the dict must be the size of the provided ``process_group``,
2025-03-14T04:33:23.6812620Z         which indicates model averaging at the highest level of the hierarchy.
2025-03-14T04:33:23.6812939Z         If ``process_group`` is not provided, then the last group size should be equal to the world size.
2025-03-14T04:33:23.6813026Z     
2025-03-14T04:33:23.6813131Z     .. warning ::
2025-03-14T04:33:23.6813374Z         `HierarchicalModelAverager` is experimental and subject to change.
2025-03-14T04:33:23.6813477Z     
2025-03-14T04:33:23.6813731Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6813894Z     
2025-03-14T04:33:23.6814002Z       warnings.warn(msg)
2025-03-14T04:33:23.6814107Z     
2025-03-14T04:33:23.6814370Z --- Parse Warning: 52 / 116 ---
2025-03-14T04:33:23.6815444Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40.
2025-03-14T04:33:23.6815712Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6815819Z     
2025-03-14T04:33:23.6816115Z     StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2025-03-14T04:33:23.6816379Z     on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2025-03-14T04:33:23.6816469Z     
2025-03-14T04:33:23.6816652Z     . N.B. Intended to be used with DynamicMetaLoadPlanner
2025-03-14T04:33:23.6816743Z     
2025-03-14T04:33:23.6816853Z     .. warning::
2025-03-14T04:33:23.6817031Z         Current implementation only supports loading Tensors.
2025-03-14T04:33:23.6817131Z     
2025-03-14T04:33:23.6817254Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6817398Z     >>> sd = {"mode": model}
2025-03-14T04:33:23.6817492Z     >>> dcp.load(
2025-03-14T04:33:23.6817598Z     >>>    sd,
2025-03-14T04:33:23.6817759Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-14T04:33:23.6817905Z     >>>    planner=DynamicMetaLoadPlanner(),
2025-03-14T04:33:23.6818029Z     >>>    checkpoint_id="path_to_model.pt"
2025-03-14T04:33:23.6818119Z     >>> )
2025-03-14T04:33:23.6818219Z     
2025-03-14T04:33:23.6818476Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6818582Z     
2025-03-14T04:33:23.6818687Z       warnings.warn(msg)
2025-03-14T04:33:23.6818791Z     
2025-03-14T04:33:23.6818990Z --- Parse Warning: 53 / 116 ---
2025-03-14T04:33:23.6820035Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151.
2025-03-14T04:33:23.6820305Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6820405Z     
2025-03-14T04:33:23.6820771Z     Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2025-03-14T04:33:23.6821111Z     avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2025-03-14T04:33:23.6821233Z     metadata file, like Torch Save files.
2025-03-14T04:33:23.6821334Z     
2025-03-14T04:33:23.6821573Z     . N.B. Intended to be used with BroadcastingTorchSaveReader
2025-03-14T04:33:23.6821678Z     
2025-03-14T04:33:23.6821773Z     .. warning::
2025-03-14T04:33:23.6821963Z         Current implementation only supports loading Tensors.
2025-03-14T04:33:23.6822048Z     
2025-03-14T04:33:23.6822182Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6822283Z     >>> sd = {"mode": model}
2025-03-14T04:33:23.6822394Z     >>> dcp.load(
2025-03-14T04:33:23.6822484Z     >>>    sd,
2025-03-14T04:33:23.6822659Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-14T04:33:23.6822790Z     >>>    planner=DynamicMetaLoadPlanner(),
2025-03-14T04:33:23.6822927Z     >>>    checkpoint_id="path_to_model.pt"
2025-03-14T04:33:23.6823015Z     >>> )
2025-03-14T04:33:23.6823113Z     
2025-03-14T04:33:23.6823367Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6823467Z     
2025-03-14T04:33:23.6823570Z       warnings.warn(msg)
2025-03-14T04:33:23.6823676Z     
2025-03-14T04:33:23.6823870Z --- Parse Warning: 54 / 116 ---
2025-03-14T04:33:23.6824919Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221.
2025-03-14T04:33:23.6825225Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6825327Z     
2025-03-14T04:33:23.6825541Z     Load a state_dict in conjunction with FSDP sharded optimizer state.
2025-03-14T04:33:23.6825645Z     
2025-03-14T04:33:23.6825815Z     This is the current recommended way to checkpoint FSDP.
2025-03-14T04:33:23.6825932Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6826094Z     >>> import torch.distributed.checkpoint as dist_cp
2025-03-14T04:33:23.6826197Z     >>> # Save
2025-03-14T04:33:23.6826305Z     >>> model: torch.nn.Model
2025-03-14T04:33:23.6826448Z     >>> optim_params = model.parameters()
2025-03-14T04:33:23.6826597Z     >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2025-03-14T04:33:23.6826708Z     >>> # Save
2025-03-14T04:33:23.6826930Z     >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2025-03-14T04:33:23.6827030Z     >>>     state_dict = {
2025-03-14T04:33:23.6827210Z     >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2025-03-14T04:33:23.6827356Z     >>>         "model": model.state_dict()
2025-03-14T04:33:23.6827463Z     >>>     }
2025-03-14T04:33:23.6827575Z     >>>     dist_cp.save_state_dict(
2025-03-14T04:33:23.6827701Z     >>>         state_dict=optim_state,
2025-03-14T04:33:23.6827888Z     >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2025-03-14T04:33:23.6828042Z     >>>         planner=dist_cp.DefaultSavePlanner(),
2025-03-14T04:33:23.6828132Z     >>>     )
2025-03-14T04:33:23.6828231Z     >>>
2025-03-14T04:33:23.6828321Z     >>> # Load
2025-03-14T04:33:23.6828566Z     >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2025-03-14T04:33:23.6828701Z     >>>     model_state_dict = model_tp.state_dict()
2025-03-14T04:33:23.6828820Z     >>>     checkpoint = {
2025-03-14T04:33:23.6828931Z     >>>         "model": model_state_dict
2025-03-14T04:33:23.6829034Z     >>>     }
2025-03-14T04:33:23.6829142Z     >>>     dist_cp.load_state_dict(
2025-03-14T04:33:23.6829268Z     >>>         state_dict=checkpoint,
2025-03-14T04:33:23.6829465Z     >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2025-03-14T04:33:23.6829618Z     >>>         planner=dist_cp.DefaultLoadPlanner(),
2025-03-14T04:33:23.6829709Z     >>>     )
2025-03-14T04:33:23.6829879Z     >>>     model.load_state_dict(checkpoint["model_state"])
2025-03-14T04:33:23.6829967Z     >>>
2025-03-14T04:33:23.6830160Z     >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2025-03-14T04:33:23.6830269Z     >>>         model_state_dict,
2025-03-14T04:33:23.6830494Z     >>>         optimizer_key="optimizer",
2025-03-14T04:33:23.6830706Z     >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2025-03-14T04:33:23.6830815Z     >>>     )
2025-03-14T04:33:23.6830904Z     >>>
2025-03-14T04:33:23.6831070Z     >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2025-03-14T04:33:23.6831202Z     >>>        model, optim, optim_state["optimizer"]
2025-03-14T04:33:23.6831307Z     >>>     )
2025-03-14T04:33:23.6831397Z     >>>
2025-03-14T04:33:23.6831540Z     >>>     optim.load_state_dict(flattened_osd)
2025-03-14T04:33:23.6831628Z     
2025-03-14T04:33:23.6831885Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6831987Z     
2025-03-14T04:33:23.6832089Z       warnings.warn(msg)
2025-03-14T04:33:23.6832192Z     
2025-03-14T04:33:23.6832390Z --- Parse Warning: 55 / 116 ---
2025-03-14T04:33:23.6833351Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=113.
2025-03-14T04:33:23.6833646Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6833748Z     
2025-03-14T04:33:23.6834039Z     Abstract class defining the protocol used by save_state_dict to plan the save process.
2025-03-14T04:33:23.6834141Z     
2025-03-14T04:33:23.6834438Z     SavePlanners are stateful objects that can be used to customize the whole save process.
2025-03-14T04:33:23.6834540Z     
2025-03-14T04:33:23.6834820Z     SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-14T04:33:23.6834957Z     will be visible to the whole process.
2025-03-14T04:33:23.6835044Z     
2025-03-14T04:33:23.6835336Z     A planner subclass can expect the following sequence of calls during save_state_dict:
2025-03-14T04:33:23.6835424Z     
2025-03-14T04:33:23.6835569Z     1) set_up_planner - called on all ranks.
2025-03-14T04:33:23.6835702Z         Signals the start of a checkpoint save.
2025-03-14T04:33:23.6835801Z     
2025-03-14T04:33:23.6835930Z     2) create_local_plan - called on all ranks.
2025-03-14T04:33:23.6836234Z         Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2025-03-14T04:33:23.6836320Z     
2025-03-14T04:33:23.6836516Z     3) create_global_plan - called on the coordinator rank only.
2025-03-14T04:33:23.6836742Z         Takes the SavePlan from all ranks and make any global decision.
2025-03-14T04:33:23.6836842Z     
2025-03-14T04:33:23.6836961Z     4) finish_plan - called on all ranks.
2025-03-14T04:33:23.6837193Z         This gives each rank a chance to adjust to global planning decisions.
2025-03-14T04:33:23.6837279Z     
2025-03-14T04:33:23.6837451Z     5) resolve_data - called multiple times on each rank
2025-03-14T04:33:23.6837657Z         Lookups a value on the `state_dict` for the storage layer to write.
2025-03-14T04:33:23.6837758Z     
2025-03-14T04:33:23.6838064Z     Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2025-03-14T04:33:23.6838264Z     most changes can be expressed by changes in a single method.
2025-03-14T04:33:23.6838351Z     
2025-03-14T04:33:23.6838491Z     There are 3 usual patterns of extension:
2025-03-14T04:33:23.6838579Z     
2025-03-14T04:33:23.6838848Z     Rewriting state_dict. This is the simplest way to extend the save process as it
2025-03-14T04:33:23.6839079Z     doesn't requite understanding the intrincacies of how SavePlan works:
2025-03-14T04:33:23.6839264Z     
2025-03-14T04:33:23.6839389Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6839544Z     >>> class RenamePlanner(DefaultSavePlanner):
2025-03-14T04:33:23.6839653Z     >>>     def set_up_planner(
2025-03-14T04:33:23.6839760Z     >>>         self,
2025-03-14T04:33:23.6839878Z     >>>         state_dict: STATE_DICT_TYPE,
2025-03-14T04:33:23.6840011Z     >>>         storage_meta: Optional[StorageMeta],
2025-03-14T04:33:23.6840197Z     >>>         is_coordinator: bool,
2025-03-14T04:33:23.6840296Z     >>>     ) -> None:
2025-03-14T04:33:23.6840424Z     >>> # prefix all keys with `foo_``
2025-03-14T04:33:23.6840724Z     >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2025-03-14T04:33:23.6840820Z     
2025-03-14T04:33:23.6841153Z     Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2025-03-14T04:33:23.6841255Z     
2025-03-14T04:33:23.6841376Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6841527Z     >>> class FP16Planner(DefaultSavePlanner):
2025-03-14T04:33:23.6841643Z     >>>     def create_local_plan(self):
2025-03-14T04:33:23.6841781Z     >>>         plan = super().create_local_plan()
2025-03-14T04:33:23.6841890Z     >>>         for p in plan:
2025-03-14T04:33:23.6842025Z     >>>             if p.tensor_data is not None:
2025-03-14T04:33:23.6842193Z     >>>                 p.tensor_data.properties.dtype = torch.float16
2025-03-14T04:33:23.6842309Z     >>>         return plan
2025-03-14T04:33:23.6842398Z     >>>
2025-03-14T04:33:23.6842560Z     >>>     def resolve_data(self, write_item):
2025-03-14T04:33:23.6842693Z     >>>         item = super().resolve_data(write_item)
2025-03-14T04:33:23.6842983Z     >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2025-03-14T04:33:23.6843069Z     
2025-03-14T04:33:23.6843431Z     Using the global planning step to make central decisions that can't be made individually by each rank
2025-03-14T04:33:23.6843519Z     
2025-03-14T04:33:23.6843651Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6843771Z     >>> from itertools import zip_longest
2025-03-14T04:33:23.6843902Z     >>> from dataclasses import replace
2025-03-14T04:33:23.6844079Z     >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2025-03-14T04:33:23.6844372Z     >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
2025-03-14T04:33:23.6844509Z     >>> # This sample doesn't handle ShardedTensors
2025-03-14T04:33:23.6844658Z     >>>     def create_global_plan(self, all_plans):
2025-03-14T04:33:23.6844817Z     >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2025-03-14T04:33:23.6844937Z     >>>         items_per_rank = [
2025-03-14T04:33:23.6845080Z     >>>             [item for item in items if item is not None]
2025-03-14T04:33:23.6845283Z     >>>             for items in zip(*zip_longest(*iters), strict=True)
2025-03-14T04:33:23.6845375Z     >>>         ]
2025-03-14T04:33:23.6845489Z     >>>         all_plans = [
2025-03-14T04:33:23.6845610Z     >>>             replace(plan, items=items)
2025-03-14T04:33:23.6845820Z     >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2025-03-14T04:33:23.6845912Z     >>>         ]
2025-03-14T04:33:23.6846075Z     >>>         return super().create_global_plan(all_plans)
2025-03-14T04:33:23.6846164Z     
2025-03-14T04:33:23.6846449Z     Finally, some planners need to save additional metadata in the checkpoint, this is
2025-03-14T04:33:23.6846725Z     accomplished by having each rank contribute their data items in the local plan and
2025-03-14T04:33:23.6846861Z     the global planner aggregate them:
2025-03-14T04:33:23.6846948Z     
2025-03-14T04:33:23.6847082Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6847244Z     >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2025-03-14T04:33:23.6847394Z     >>>     def create_local_plan(self) -> SavePlan:
2025-03-14T04:33:23.6847520Z     >>>         plan = super().create_local_plan()
2025-03-14T04:33:23.6847704Z     >>>         return replace(plan, planner_data="per-rank-data")
2025-03-14T04:33:23.6847794Z     >>>
2025-03-14T04:33:23.6848091Z     >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2025-03-14T04:33:23.6848308Z     >>>         global_plan, metadata = super().create_global_plan(all_plans)
2025-03-14T04:33:23.6848553Z     >>>         merged_data = [p.planner_data for p in global_plan]
2025-03-14T04:33:23.6848944Z     >>>         metadata = replace(metadata, planner_data=merged_data)
2025-03-14T04:33:23.6849083Z     >>>         return global_plan, metadata
2025-03-14T04:33:23.6849169Z     
2025-03-14T04:33:23.6849428Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6849531Z     
2025-03-14T04:33:23.6849637Z       warnings.warn(msg)
2025-03-14T04:33:23.6849738Z     
2025-03-14T04:33:23.6849967Z --- Parse Warning: 56 / 116 ---
2025-03-14T04:33:23.6850927Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=293.
2025-03-14T04:33:23.6851193Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6851294Z     
2025-03-14T04:33:23.6851584Z     Abstract class defining the protocol used by load_state_dict to plan the load process.
2025-03-14T04:33:23.6851761Z     
2025-03-14T04:33:23.6852048Z     LoadPlanner are stateful objects that can be used to customize the whole load process.
2025-03-14T04:33:23.6852149Z     
2025-03-14T04:33:23.6852429Z     LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-14T04:33:23.6852569Z     will be visible to the whole process.
2025-03-14T04:33:23.6852655Z     
2025-03-14T04:33:23.6852949Z     A planner subclass can expect the following sequence of calls during load_state_dict:
2025-03-14T04:33:23.6853035Z     
2025-03-14T04:33:23.6853178Z     1) set_up_planner - called on all ranks.
2025-03-14T04:33:23.6853315Z         Signals the start of loading a checkpoint.
2025-03-14T04:33:23.6853416Z     
2025-03-14T04:33:23.6853547Z     2) create_local_plan - called on all ranks.
2025-03-14T04:33:23.6853851Z         Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2025-03-14T04:33:23.6853940Z     
2025-03-14T04:33:23.6854141Z     3) create_global_plan - called on the coordinator rank only.
2025-03-14T04:33:23.6854343Z         Takes the LoadPlan from all ranks and make any global decision.
2025-03-14T04:33:23.6854445Z     
2025-03-14T04:33:23.6854599Z     4) load_bytes - called multiple times on each rank
2025-03-14T04:33:23.6854787Z         This is called once per non-tensor value in state_dict.
2025-03-14T04:33:23.6854909Z     
2025-03-14T04:33:23.6855155Z     5) resolve_tensor and commit_tensor - called multiple times on each rank
2025-03-14T04:33:23.6855341Z         They are called in pair for each Tensor value in state_dict.
2025-03-14T04:33:23.6855442Z     
2025-03-14T04:33:23.6855744Z     Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2025-03-14T04:33:23.6855946Z     most changes can be expressed by changes in a single method.
2025-03-14T04:33:23.6856034Z     
2025-03-14T04:33:23.6856184Z     There are two usual patterns of extension:
2025-03-14T04:33:23.6856271Z     
2025-03-14T04:33:23.6856542Z     Rewriting state_dict. This is the simplest way to extend the load process as it
2025-03-14T04:33:23.6856801Z     doesn't requite understanding the intrincacies of how LoadPlan works. We need
2025-03-14T04:33:23.6857043Z     to keep a reference to the original state_dict as load happens in place so
2025-03-14T04:33:23.6857186Z     we need to be able to perform it in place
2025-03-14T04:33:23.6857289Z     
2025-03-14T04:33:23.6857412Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6857564Z     >>> class RenamePlanner(DefaultLoadPlanner):
2025-03-14T04:33:23.6857670Z     >>>     def set_up_planner(
2025-03-14T04:33:23.6857774Z     >>>         self,
2025-03-14T04:33:23.6857892Z     >>>         state_dict: STATE_DICT_TYPE,
2025-03-14T04:33:23.6858002Z     >>>         metadata: Metadata,
2025-03-14T04:33:23.6858126Z     >>>         is_coordinator: bool,
2025-03-14T04:33:23.6858301Z     >>>     ) -> None:
2025-03-14T04:33:23.6858431Z     >>>         self.original_state_dict = state_dict
2025-03-14T04:33:23.6858625Z     >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2025-03-14T04:33:23.6858711Z     >>>
2025-03-14T04:33:23.6858849Z     >>>         if self.flatten_sharded_tensors:
2025-03-14T04:33:23.6859006Z     >>>             state_dict = _flatten_sharded_tensors(state_dict)
2025-03-14T04:33:23.6859109Z     >>>
2025-03-14T04:33:23.6859226Z     >>>         if self.flatten_state_dict:
2025-03-14T04:33:23.6859429Z     >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2025-03-14T04:33:23.6859516Z     >>>
2025-03-14T04:33:23.6859647Z     >>>         self.state_dict = state_dict
2025-03-14T04:33:23.6859758Z     >>>         self.metadata = metadata
2025-03-14T04:33:23.6859895Z     >>>         self.is_coordinator = is_coordinator
2025-03-14T04:33:23.6859982Z     >>>
2025-03-14T04:33:23.6860119Z     >>>     def load_bytes(self, read_item, value):
2025-03-14T04:33:23.6860232Z     >>> # Remove the "foo_" prefix
2025-03-14T04:33:23.6860561Z     >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2025-03-14T04:33:23.6860674Z     
2025-03-14T04:33:23.6860769Z     
2025-03-14T04:33:23.6861034Z     Modifying resolve_tensor and commit_tensor to handle load time transformation.
2025-03-14T04:33:23.6861127Z     
2025-03-14T04:33:23.6861248Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.6861419Z     >>> class MetaModelMaterialize(DefaultSavePlanner):
2025-03-14T04:33:23.6861545Z     >>>     def resolve_tensor(self, read_item):
2025-03-14T04:33:23.6861692Z     >>>         tensor = super().resolve_tensor(read_item)
2025-03-14T04:33:23.6861846Z     >>>         return torch.empty_like(tensor, device="cpu")
2025-03-14T04:33:23.6861941Z     >>>
2025-03-14T04:33:23.6862077Z     >>>     def commit_tensor(self, read_item, tensor):
2025-03-14T04:33:23.6862251Z     >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2025-03-14T04:33:23.6862341Z     
2025-03-14T04:33:23.6862603Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6862692Z     
2025-03-14T04:33:23.6862799Z       warnings.warn(msg)
2025-03-14T04:33:23.6862885Z     
2025-03-14T04:33:23.6863096Z --- Parse Warning: 57 / 116 ---
2025-03-14T04:33:23.6864093Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1124.
2025-03-14T04:33:23.6864365Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6864452Z     
2025-03-14T04:33:23.6864630Z     Return the model state_dict and optimizers state_dict.
2025-03-14T04:33:23.6864716Z     
2025-03-14T04:33:23.6864952Z     ``get_state_dict`` can process any module that is parallelized by PyTorch
2025-03-14T04:33:23.6865210Z     FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
2025-03-14T04:33:23.6865472Z     combination of these parallelisms. The main functions of ``get_state_dict``
2025-03-14T04:33:23.6865693Z     are: 1.) returning a model and optimizer state_dict that can be resharded
2025-03-14T04:33:23.6865914Z     with a different number of trainers and/or different parallelisms.
2025-03-14T04:33:23.6866170Z     2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
2025-03-14T04:33:23.6866273Z     these APIs.
2025-03-14T04:33:23.6866401Z     3.) sanity checking the result state_dict.
2025-03-14T04:33:23.6866495Z     
2025-03-14T04:33:23.6866707Z     The keys of the result state dictionary are the canonical FQNs (Fully
2025-03-14T04:33:23.6866956Z     Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
2025-03-14T04:33:23.6867221Z     position in an nn.Module hierarchy. More specifically, a canonical FQN to a
2025-03-14T04:33:23.6867464Z     parameter is the FQN returned by ``module.named_parameters()`` or
2025-03-14T04:33:23.6867673Z     ``module.named_buffers()`` when the module is not distributed by any
2025-03-14T04:33:23.6867940Z     parallelisms. Since the optimizer internally uses parameter IDs to represent
2025-03-14T04:33:23.6868157Z     a parameter, there will be a conversion from the parameter IDs to the
2025-03-14T04:33:23.6868284Z     canonical FQNs when calling this API.
2025-03-14T04:33:23.6868369Z     
2025-03-14T04:33:23.6868600Z     ``get_state_dict`` can also process a module that is not parallelized. In
2025-03-14T04:33:23.6868822Z     such a case, ``get_state_dict`` only performs one function -- converting the
2025-03-14T04:33:23.6868978Z     optimizer parameter IDs to the canonical FQNs.
2025-03-14T04:33:23.6869061Z     
2025-03-14T04:33:23.6869161Z     Example:
2025-03-14T04:33:23.6869263Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6869361Z         >>> import torch
2025-03-14T04:33:23.6869615Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:23.6869820Z         >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-14T04:33:23.6870080Z         >>> from torch.distributed.checkpoint.state_dict import get_state_dict
2025-03-14T04:33:23.6870167Z     
2025-03-14T04:33:23.6870309Z         >>> fsdp_model = FSDP(copy.deepcopy(model))
2025-03-14T04:33:23.6870502Z         >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-14T04:33:23.6870636Z         >>> ddp_model = DDP(copy.deepcopy(model))
2025-03-14T04:33:23.6870818Z         >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-14T04:33:23.6870912Z     
2025-03-14T04:33:23.6870999Z     
2025-03-14T04:33:23.6871246Z         >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
2025-03-14T04:33:23.6871419Z         >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
2025-03-14T04:33:23.6871540Z         ...     fsdp_model, fsdp_optim
2025-03-14T04:33:23.6871629Z         ... )
2025-03-14T04:33:23.6871719Z     
2025-03-14T04:33:23.6871935Z         >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
2025-03-14T04:33:23.6872049Z         >>> # the asserts will fail.
2025-03-14T04:33:23.6872183Z         >>> assert ddp_state_dict == fsdp_state_dict
2025-03-14T04:33:23.6872341Z         >>> assert ddp_optim_state == fsdp_optim_state_dict
2025-03-14T04:33:23.6872426Z     
2025-03-14T04:33:23.6872541Z     
2025-03-14T04:33:23.6872630Z     Args:
2025-03-14T04:33:23.6872782Z         model (nn.Module): the nn.Module to the model.
2025-03-14T04:33:23.6872976Z         optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
2025-03-14T04:33:23.6873147Z             The optimizers that are used to optimize ``model``.
2025-03-14T04:33:23.6873429Z         submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
2025-03-14T04:33:23.6873553Z             that belong to the submodules.
2025-03-14T04:33:23.6873733Z         options (StateDictOptions): the options to control how
2025-03-14T04:33:23.6873950Z             model state_dict and optimizer state_dict should be returned. See
2025-03-14T04:33:23.6874078Z             `StateDictOptions` for the details.
2025-03-14T04:33:23.6874170Z     
2025-03-14T04:33:23.6874264Z     Returns:
2025-03-14T04:33:23.6874470Z         ``Tuple`` that contain model state_dict and optimizer state_dict.
2025-03-14T04:33:23.6874557Z     
2025-03-14T04:33:23.6874795Z     :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
2025-03-14T04:33:23.6874880Z     
2025-03-14T04:33:23.6875139Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6875224Z     
2025-03-14T04:33:23.6875335Z       warnings.warn(msg)
2025-03-14T04:33:23.6875425Z     
2025-03-14T04:33:23.6875621Z --- Parse Warning: 58 / 116 ---
2025-03-14T04:33:23.6876609Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=62.
2025-03-14T04:33:23.6876912Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6876999Z     
2025-03-14T04:33:23.6877206Z     Load a checkpoint into a distributed state dict in SPMD style.
2025-03-14T04:33:23.6877294Z     
2025-03-14T04:33:23.6877527Z     Each rank must have the same keys in their ``state_dict`` provided to this
2025-03-14T04:33:23.6877759Z     API. Mismatched keys may result in hangs or errors. If unsure, you can use
2025-03-14T04:33:23.6877984Z     the ``utils._assert_same_keys`` API to check (but may incur communication
2025-03-14T04:33:23.6878074Z     costs).
2025-03-14T04:33:23.6878154Z     
2025-03-14T04:33:23.6878345Z     Each rank will try to read the least amount of data necessary
2025-03-14T04:33:23.6878577Z     to fulfill the requested `state_dict`. When loading :class:`ShardedTensor`
2025-03-14T04:33:23.6878841Z     or :class:`DTensor` instances, each rank only reads data for their local shards.
2025-03-14T04:33:23.6878949Z     
2025-03-14T04:33:23.6879331Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-14T04:33:23.6879586Z     load will first call ``state_dict`` before attempting deserialization, followed by
2025-03-14T04:33:23.6879773Z     ``load_state_dict`` once the deserialization is complete.
2025-03-14T04:33:23.6880026Z     For each non-``Stateful`` object, load will deserailize the object, and then replace
2025-03-14T04:33:23.6880196Z     it in the ``state_dict`` with the deserialized object.
2025-03-14T04:33:23.6880280Z     
2025-03-14T04:33:23.6880393Z     .. warning::
2025-03-14T04:33:23.6880562Z         All tensors in ``state_dict`` must be allocated on their
2025-03-14T04:33:23.6880741Z         destination device *prior to* calling this function.
2025-03-14T04:33:23.6880828Z     
2025-03-14T04:33:23.6881066Z         All non-tensor data is loaded using `torch.load()` and modified in place
2025-03-14T04:33:23.6881162Z         on state_dict.
2025-03-14T04:33:23.6881258Z     
2025-03-14T04:33:23.6881351Z     .. warning::
2025-03-14T04:33:23.6881571Z         Users must call `load_state_dict` on the root module to ensure load
2025-03-14T04:33:23.6881756Z         pos-processing and non-tensor data properly propagates.
2025-03-14T04:33:23.6881851Z     
2025-03-14T04:33:23.6881973Z     .. note:
2025-03-14T04:33:23.6882211Z         If no process group is initialized, this function will assume the intent
2025-03-14T04:33:23.6882436Z         is to load a checkpoint into the local process. This can be useful in the
2025-03-14T04:33:23.6882693Z         case of local inference, and when using regular Tensors (as opposed to DTensor
2025-03-14T04:33:23.6882797Z          or ShardedTensor)
2025-03-14T04:33:23.6882891Z     
2025-03-14T04:33:23.6882978Z     .. note:
2025-03-14T04:33:23.6883129Z         Rank 0 is assumed to be the coordinator rank.
2025-03-14T04:33:23.6883213Z     
2025-03-14T04:33:23.6883310Z     Args:
2025-03-14T04:33:23.6883522Z         state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
2025-03-14T04:33:23.6883678Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-03-14T04:33:23.6883887Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-14T04:33:23.6884100Z             depends on the storage. It can be a path to a folder or to a file.
2025-03-14T04:33:23.6884269Z             It can also be a key if the storage is a key-value store.
2025-03-14T04:33:23.6884379Z             (Default: ``None``)
2025-03-14T04:33:23.6884514Z         storage_reader (Optional[StorageReader]):
2025-03-14T04:33:23.6884727Z             Instance of StorageWriter used to perform reads. If this is not
2025-03-14T04:33:23.6884932Z             specified, DCP will automatically infer the reader based on the
2025-03-14T04:33:23.6885184Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-14T04:33:23.6885324Z             be raised. (Default: ``None``)
2025-03-14T04:33:23.6885455Z         planner (Optional[LoadPlanner]):
2025-03-14T04:33:23.6885659Z             Instance of LoadPlanner. If this is not specificed, the default
2025-03-14T04:33:23.6885801Z             planner will be used. (Default: ``None``)
2025-03-14T04:33:23.6885934Z         process_group (Optional[ProcessGroup]):
2025-03-14T04:33:23.6886127Z             ProcessGroup to be used for cross-rank synchronization.
2025-03-14T04:33:23.6886227Z             (Default: ``None``)
2025-03-14T04:33:23.6886447Z         no_dist (bool): If ``True``, this function will assume the intent is to load
2025-03-14T04:33:23.6886699Z             a checkpoint without using cross-rank synchronization. (Default: ``False``)
2025-03-14T04:33:23.6886800Z     Returns:
2025-03-14T04:33:23.6886888Z         None.
2025-03-14T04:33:23.6886979Z     
2025-03-14T04:33:23.6887069Z     Examples
2025-03-14T04:33:23.6887173Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6887289Z         >>> my_model = MyModule()
2025-03-14T04:33:23.6887429Z         >>> optimizer = Adagrad(my_model.parameters())
2025-03-14T04:33:23.6887604Z         >>> model_state_dict = my_model.state_dict()
2025-03-14T04:33:23.6887842Z         >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
2025-03-14T04:33:23.6887957Z         ...     "/checkpoint/1"
2025-03-14T04:33:23.6888047Z         ... )
2025-03-14T04:33:23.6888147Z     
2025-03-14T04:33:23.6888310Z         >>> torch.distributed.checkpoint.load_state_dict(
2025-03-14T04:33:23.6888443Z         >>>     state_dict=model_state_dict,
2025-03-14T04:33:23.6888568Z         >>>     storage_reader=fs_storage_reader,
2025-03-14T04:33:23.6888670Z         >>> )
2025-03-14T04:33:23.6888757Z     
2025-03-14T04:33:23.6888972Z         >>> # module.load_state_dict() function might have customized steps
2025-03-14T04:33:23.6889105Z         >>> # to flush the state_dict, must call it to
2025-03-14T04:33:23.6889239Z         >>> # ensure correct behavior.
2025-03-14T04:33:23.6889377Z         >>> my_model.load_state_dict(model_state_dict)
2025-03-14T04:33:23.6889482Z     
2025-03-14T04:33:23.6889575Z     .. note::
2025-03-14T04:33:23.6889804Z         load_state_dict uses collectives to coordinate reads across ranks.
2025-03-14T04:33:23.6890024Z         For NCCL-based process groups, internal tensor representations of
2025-03-14T04:33:23.6890298Z         objects must be moved to the GPU device before communication takes place.
2025-03-14T04:33:23.6890523Z         In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-14T04:33:23.6890766Z         and it is the user's responsibility to ensure that this is set so that each
2025-03-14T04:33:23.6890956Z         rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-14T04:33:23.6891061Z     
2025-03-14T04:33:23.6891318Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6891424Z     
2025-03-14T04:33:23.6891532Z       warnings.warn(msg)
2025-03-14T04:33:23.6891635Z     
2025-03-14T04:33:23.6891854Z --- Parse Warning: 59 / 116 ---
2025-03-14T04:33:23.6892813Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=85.
2025-03-14T04:33:23.6893081Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6893183Z     
2025-03-14T04:33:23.6893313Z     Save a distributed model in SPMD style.
2025-03-14T04:33:23.6893416Z     
2025-03-14T04:33:23.6893615Z     This function is different from ``torch.save()`` as it handles
2025-03-14T04:33:23.6893890Z     ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2025-03-14T04:33:23.6893979Z     
2025-03-14T04:33:23.6894282Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-14T04:33:23.6894463Z     save will call ``state_dict`` before serialization.
2025-03-14T04:33:23.6894564Z     
2025-03-14T04:33:23.6894661Z     .. warning::
2025-03-14T04:33:23.6894915Z         There is no guarantees of Backwards Compatibility across PyTorch versions
2025-03-14T04:33:23.6895024Z         for saved state_dicts.
2025-03-14T04:33:23.6895127Z     
2025-03-14T04:33:23.6895221Z     .. warning::
2025-03-14T04:33:23.6895438Z         If using the `process_group` argument, make sure that only its ranks
2025-03-14T04:33:23.6895664Z         call `save_state_dict` and that all data in state_dict belong to it.
2025-03-14T04:33:23.6895752Z     
2025-03-14T04:33:23.6895862Z     .. note::
2025-03-14T04:33:23.6896126Z         When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2025-03-14T04:33:23.6896398Z         the shard_group should be calling `save_state_dict` and the corresponding process
2025-03-14T04:33:23.6896518Z         group needs to be passed in.
2025-03-14T04:33:23.6896619Z     
2025-03-14T04:33:23.6896708Z     .. note::
2025-03-14T04:33:23.6897014Z         If no process group is available, this function assumes the intention is to save the
2025-03-14T04:33:23.6897133Z          state_dict in the local process.
2025-03-14T04:33:23.6897229Z     
2025-03-14T04:33:23.6897318Z     .. note:
2025-03-14T04:33:23.6897473Z         Rank 0 is assumed to be the coordinator rank.
2025-03-14T04:33:23.6897561Z     
2025-03-14T04:33:23.6897657Z     
2025-03-14T04:33:23.6897748Z     Args:
2025-03-14T04:33:23.6897920Z         state_dict (Dict[str, Any]): The state_dict to save.
2025-03-14T04:33:23.6898069Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-03-14T04:33:23.6898294Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-14T04:33:23.6898500Z             depends on the storage. It can be a path to a folder or to a file.
2025-03-14T04:33:23.6898683Z             It can also be a key if the storage is a key-value store.
2025-03-14T04:33:23.6898790Z             (Default: ``None``)
2025-03-14T04:33:23.6898938Z         storage_writer (Optional[StorageWriter]):
2025-03-14T04:33:23.6899152Z             Instance of StorageWriter used to perform writes. If this is not
2025-03-14T04:33:23.6899373Z             specified, DCP will automatically infer the writer based on the
2025-03-14T04:33:23.6899605Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-14T04:33:23.6899736Z             be raised. (Default: ``None``)
2025-03-14T04:33:23.6899857Z         planner (Optional[SavePlanner]):
2025-03-14T04:33:23.6900077Z             Instance of SavePlanner. If this is not specificed, the default
2025-03-14T04:33:23.6900210Z             planner will be used. (Default: ``None``)
2025-03-14T04:33:23.6900361Z         process_group (Optional[ProcessGroup]):
2025-03-14T04:33:23.6900550Z             ProcessGroup to be used for cross-rank synchronization.
2025-03-14T04:33:23.6900673Z             (Default: ``None``)
2025-03-14T04:33:23.6900773Z         no_dist (bool):
2025-03-14T04:33:23.6900962Z             If ``True``, this function will assume the intent is to load
2025-03-14T04:33:23.6901140Z             a checkpoint without using cross-rank synchronization.
2025-03-14T04:33:23.6901258Z             (Default: ``False``)
2025-03-14T04:33:23.6901348Z     
2025-03-14T04:33:23.6901450Z     Returns:
2025-03-14T04:33:23.6901616Z         Metadata: Metadata object for the saved checkpoint.
2025-03-14T04:33:23.6901715Z     
2025-03-14T04:33:23.6901807Z     Example:
2025-03-14T04:33:23.6901911Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6902031Z         >>> my_model = MyModule()
2025-03-14T04:33:23.6902117Z     
2025-03-14T04:33:23.6902248Z         >>> state_dict = {"model": my_model}
2025-03-14T04:33:23.6902334Z     
2025-03-14T04:33:23.6902582Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-14T04:33:23.6902687Z         ...     "/checkpoint/1"
2025-03-14T04:33:23.6902840Z         ... )
2025-03-14T04:33:23.6902981Z         >>> torch.distributed.checkpoint.save(
2025-03-14T04:33:23.6903108Z         >>>     state_dict=state_dict,
2025-03-14T04:33:23.6903234Z         >>>     storage_writer=fs_storage_writer,
2025-03-14T04:33:23.6903337Z         >>> )
2025-03-14T04:33:23.6903424Z     
2025-03-14T04:33:23.6903527Z     .. note::
2025-03-14T04:33:23.6903743Z         save_state_dict uses collectives to coordinate writes across ranks.
2025-03-14T04:33:23.6903977Z         For NCCL-based process groups, internal tensor representations of
2025-03-14T04:33:23.6904211Z         objects must be moved to the GPU device before communication takes place.
2025-03-14T04:33:23.6904447Z         In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-14T04:33:23.6904655Z         and it is the user's responsibility to ensure that this is set so that
2025-03-14T04:33:23.6904868Z         each rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-14T04:33:23.6904956Z     
2025-03-14T04:33:23.6905226Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6905338Z     
2025-03-14T04:33:23.6905455Z       warnings.warn(msg)
2025-03-14T04:33:23.6905544Z     
2025-03-14T04:33:23.6905761Z --- Parse Warning: 60 / 116 ---
2025-03-14T04:33:23.6906738Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=195.
2025-03-14T04:33:23.6907020Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6907289Z     Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2025-03-14T04:33:23.6907594Z         staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2025-03-14T04:33:23.6907683Z     
2025-03-14T04:33:23.6907797Z         .. warning::
2025-03-14T04:33:23.6907962Z             This feature is experimental and subject to change.
2025-03-14T04:33:23.6908066Z     
2025-03-14T04:33:23.6908154Z         Args:
2025-03-14T04:33:23.6908331Z             state_dict (Dict[str, Any]): The state_dict to save.
2025-03-14T04:33:23.6908483Z             checkpoint_id (Union[str, os.PathLike, None]):
2025-03-14T04:33:23.6908739Z                 The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-14T04:33:23.6908947Z                 depends on the storage. It can be a path to a folder or to a file.
2025-03-14T04:33:23.6909132Z                 It can also be a key if the storage is a key-value store.
2025-03-14T04:33:23.6909237Z                 (Default: ``None``)
2025-03-14T04:33:23.6909393Z             storage_writer (Optional[StorageWriter]):
2025-03-14T04:33:23.6909608Z                 Instance of StorageWriter used to perform 'stage' and  'save'. If
2025-03-14T04:33:23.6909867Z                 this is not specified, DCP will automatically infer the writer based on the
2025-03-14T04:33:23.6910075Z                 checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-14T04:33:23.6910208Z                 be raised. (Default: ``None``)
2025-03-14T04:33:23.6910333Z             planner (Optional[SavePlanner]):
2025-03-14T04:33:23.6910554Z                 Instance of SavePlanner. If this is not specificed, the default
2025-03-14T04:33:23.6910693Z                 planner will be used. (Default: ``None``)
2025-03-14T04:33:23.6910841Z             process_group (Optional[ProcessGroup]):
2025-03-14T04:33:23.6911028Z                 ProcessGroup to be used for cross-rank synchronization.
2025-03-14T04:33:23.6911143Z                 (Default: ``None``)
2025-03-14T04:33:23.6911229Z     
2025-03-14T04:33:23.6911368Z         Returns:
2025-03-14T04:33:23.6911584Z             Future: A future holding the resultant Metadata object from `save`.
2025-03-14T04:33:23.6911682Z     
2025-03-14T04:33:23.6911827Z         Example:
2025-03-14T04:33:23.6911934Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.6912056Z             >>> my_model = MyModule()
2025-03-14T04:33:23.6912143Z     
2025-03-14T04:33:23.6912276Z             >>> state_dict = {"model": my_model}
2025-03-14T04:33:23.6912360Z     
2025-03-14T04:33:23.6912608Z             >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-14T04:33:23.6912712Z             ...     "/checkpoint/1"
2025-03-14T04:33:23.6912815Z             ... )
2025-03-14T04:33:23.6913029Z             >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2025-03-14T04:33:23.6913159Z             >>>     state_dict=state_dict,
2025-03-14T04:33:23.6913286Z             >>>     storage_writer=fs_storage_writer,
2025-03-14T04:33:23.6913389Z             >>> )
2025-03-14T04:33:23.6913479Z             >>>
2025-03-14T04:33:23.6913599Z             >>> # ... do some work ...
2025-03-14T04:33:23.6913687Z             >>>
2025-03-14T04:33:23.6913824Z             >>> checkpoint_future.result()
2025-03-14T04:33:23.6913913Z     
2025-03-14T04:33:23.6914013Z         
2025-03-14T04:33:23.6914269Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6914395Z     
2025-03-14T04:33:23.6914499Z       warnings.warn(msg)
2025-03-14T04:33:23.6914598Z     
2025-03-14T04:33:23.6914797Z --- Parse Warning: 61 / 116 ---
2025-03-14T04:33:23.6915843Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=94.
2025-03-14T04:33:23.6916107Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6916208Z     
2025-03-14T04:33:23.6916413Z     Initialize rendezvous event object and record its operations.
2025-03-14T04:33:23.6916512Z     
2025-03-14T04:33:23.6916606Z     Args:
2025-03-14T04:33:23.6916752Z         run_id (str): The run id of the rendezvous.
2025-03-14T04:33:23.6916906Z         message (str): The message describing the event.
2025-03-14T04:33:23.6917178Z         node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2025-03-14T04:33:23.6917362Z         name (str): Event name. (E.g. Current action being performed).
2025-03-14T04:33:23.6917498Z         hostname (str): Hostname of the node.
2025-03-14T04:33:23.6917694Z         pid (Optional[int]): The process id of the node.
2025-03-14T04:33:23.6917960Z         master_endpoint (str): The master endpoint for the rendezvous store, if known.
2025-03-14T04:33:23.6918232Z         local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2025-03-14T04:33:23.6918414Z         rank (Optional[int]): The rank of the node, if known.
2025-03-14T04:33:23.6918505Z     Returns:
2025-03-14T04:33:23.6918613Z         None
2025-03-14T04:33:23.6918705Z     Example:
2025-03-14T04:33:23.6918859Z         >>> # See DynamicRendezvousHandler class
2025-03-14T04:33:23.6918957Z         >>> def _record(
2025-03-14T04:33:23.6919054Z         ...     self,
2025-03-14T04:33:23.6919261Z         ...     message: str,
2025-03-14T04:33:23.6919414Z         ...     node_state: NodeState = NodeState.RUNNING,
2025-03-14T04:33:23.6919545Z         ...     rank: Optional[int] = None,
2025-03-14T04:33:23.6919639Z         ... ) -> None:
2025-03-14T04:33:23.6919786Z         ...     construct_and_record_rdzv_event(
2025-03-14T04:33:23.6919960Z         ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2025-03-14T04:33:23.6920102Z         ...         run_id=self._settings.run_id,
2025-03-14T04:33:23.6920208Z         ...         message=message,
2025-03-14T04:33:23.6920337Z         ...         node_state=node_state,
2025-03-14T04:33:23.6920463Z         ...         hostname=self._this_node.addr,
2025-03-14T04:33:23.6920595Z         ...         pid=self._this_node.pid,
2025-03-14T04:33:23.6920787Z         ...         local_id=self._this_node.local_id,
2025-03-14T04:33:23.6920905Z         ...         rank=rank,
2025-03-14T04:33:23.6920997Z         ...     )
2025-03-14T04:33:23.6921097Z     
2025-03-14T04:33:23.6921353Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6921456Z     
2025-03-14T04:33:23.6921559Z       warnings.warn(msg)
2025-03-14T04:33:23.6921661Z     
2025-03-14T04:33:23.6921871Z --- Parse Warning: 62 / 116 ---
2025-03-14T04:33:23.6922791Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114.
2025-03-14T04:33:23.6923054Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6923157Z     
2025-03-14T04:33:23.6923327Z     This configures FSDP-native mixed precision training.
2025-03-14T04:33:23.6923430Z     
2025-03-14T04:33:23.6923525Z     Attributes:
2025-03-14T04:33:23.6923781Z         param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2025-03-14T04:33:23.6924017Z             parameters during forward and backward and thus the dtype for
2025-03-14T04:33:23.6924254Z             forward and backward computation. Outside forward and backward, the
2025-03-14T04:33:23.6924454Z             *sharded* parameters are kept in full precision (e.g. for the
2025-03-14T04:33:23.6924678Z             optimizer step), and for model checkpointing, the parameters are
2025-03-14T04:33:23.6924838Z             always saved in full precision. (Default: ``None``)
2025-03-14T04:33:23.6925070Z         reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-14T04:33:23.6925287Z             gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2025-03-14T04:33:23.6925479Z             ``None`` but ``param_dtype`` is not ``None``, then this takes on
2025-03-14T04:33:23.6925685Z             the ``param_dtype`` value, still running gradient reduction in low
2025-03-14T04:33:23.6925912Z             precision. This is permitted to differ from ``param_dtype``, e.g.
2025-03-14T04:33:23.6926114Z             to force gradient reduction to run in full precision. (Default:
2025-03-14T04:33:23.6926217Z             ``None``)
2025-03-14T04:33:23.6926430Z         buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-14T04:33:23.6926669Z             buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2025-03-14T04:33:23.6926868Z             ``buffer_dtype`` in the first forward pass and keeps them in that
2025-03-14T04:33:23.6927092Z             dtype thereafter. For model checkpointing, the buffers are saved
2025-03-14T04:33:23.6927276Z             in full precision except for ``LOCAL_STATE_DICT``. (Default:
2025-03-14T04:33:23.6927379Z             ``None``)
2025-03-14T04:33:23.6927576Z         keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2025-03-14T04:33:23.6927810Z             gradients to full precision after the backward pass in preparation
2025-03-14T04:33:23.6928015Z             for the optimizer step. If ``True``, then FSDP keeps the gradients
2025-03-14T04:33:23.6928231Z             in the dtype used for gradient reduction, which can save memory if
2025-03-14T04:33:23.6928439Z             using a custom optimizer that supports running in low precision.
2025-03-14T04:33:23.6928558Z             (Default: ``False``)
2025-03-14T04:33:23.6928771Z         cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2025-03-14T04:33:23.6928986Z             its forward args and kwargs to ``param_dtype``. This is to ensure
2025-03-14T04:33:23.6929203Z             that parameter and input dtypes match for forward computation, as
2025-03-14T04:33:23.6929426Z             required by many ops. This may need to be set to ``True`` when only
2025-03-14T04:33:23.6929669Z             applying mixed precision to some but not all FSDP modules, in which
2025-03-14T04:33:23.6929919Z             case a mixed-precision FSDP submodule needs to recast its inputs.
2025-03-14T04:33:23.6930023Z             (Default: ``False``)
2025-03-14T04:33:23.6930257Z         cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2025-03-14T04:33:23.6930461Z             casts its forward args and kwargs to ``param_dtype``, overriding
2025-03-14T04:33:23.6930665Z             the value of ``cast_forward_inputs``. For non-root FSDP modules,
2025-03-14T04:33:23.6930814Z             this does not do anything. (Default: ``True``)
2025-03-14T04:33:23.6931045Z         _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2025-03-14T04:33:23.6931228Z             module classes to ignore for mixed precision when using an
2025-03-14T04:33:23.6931423Z             ``auto_wrap_policy``: Modules of these classes will have FSDP
2025-03-14T04:33:23.6931635Z             applied to them separately with mixed precision disabled (meaning
2025-03-14T04:33:23.6931857Z             that the final FSDP construction would deviate from the specified
2025-03-14T04:33:23.6932051Z             policy). If ``auto_wrap_policy`` is not specified, then this does
2025-03-14T04:33:23.6932292Z             not do anything. This API is experimental and subject to change.
2025-03-14T04:33:23.6932406Z             (Default: ``(_BatchNorm,)``)
2025-03-14T04:33:23.6932504Z     
2025-03-14T04:33:23.6932677Z     .. note:: This API is experimental and subject to change.
2025-03-14T04:33:23.6932779Z     
2025-03-14T04:33:23.6933002Z     .. note:: Only floating point tensors are cast to their specified dtypes.
2025-03-14T04:33:23.6933102Z     
2025-03-14T04:33:23.6933286Z     .. note:: In ``summon_full_params``, parameters are forced to full
2025-03-14T04:33:23.6933414Z         precision, but buffers are not.
2025-03-14T04:33:23.6933500Z     
2025-03-14T04:33:23.6933720Z     .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2025-03-14T04:33:23.6933935Z         their inputs are in a low precision like ``float16`` or ``bfloat16``.
2025-03-14T04:33:23.6934183Z         Disabling FSDP's mixed precision for those norm modules only means that
2025-03-14T04:33:23.6934400Z         the affine parameters are kept in ``float32``. However, this incurs
2025-03-14T04:33:23.6934648Z         separate all-gathers and reduce-scatters for those norm modules, which
2025-03-14T04:33:23.6934866Z         may be inefficient, so if the workload permits, the user should prefer
2025-03-14T04:33:23.6935056Z         to still apply mixed precision to those modules.
2025-03-14T04:33:23.6935143Z     
2025-03-14T04:33:23.6935363Z     .. note:: By default, if the user passes a model with any ``_BatchNorm``
2025-03-14T04:33:23.6935572Z         modules and specifies an ``auto_wrap_policy``, then the batch norm
2025-03-14T04:33:23.6935811Z         modules will have FSDP applied to them separately with mixed precision
2025-03-14T04:33:23.6935991Z         disabled. See the ``_module_classes_to_ignore`` argument.
2025-03-14T04:33:23.6936091Z     
2025-03-14T04:33:23.6936300Z     .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2025-03-14T04:33:23.6936528Z         ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2025-03-14T04:33:23.6936703Z         its ``cast_root_forward_inputs`` takes precedence over its
2025-03-14T04:33:23.6936895Z         ``cast_forward_inputs``. For non-root FSDP instances, their
2025-03-14T04:33:23.6937113Z         ``cast_root_forward_inputs`` values are ignored. The default setting is
2025-03-14T04:33:23.6937346Z         sufficient for the typical case where each FSDP instance has the same
2025-03-14T04:33:23.6937572Z         ``MixedPrecision`` configuration and only needs to cast inputs to the
2025-03-14T04:33:23.6937766Z         ``param_dtype`` at the beginning of the model's forward pass.
2025-03-14T04:33:23.6937852Z     
2025-03-14T04:33:23.6938071Z     .. note:: For nested FSDP instances with different ``MixedPrecision``
2025-03-14T04:33:23.6938361Z         configurations, we recommend setting individual ``cast_forward_inputs``
2025-03-14T04:33:23.6938578Z         values to configure casting inputs or not before each instance's
2025-03-14T04:33:23.6938778Z         forward. In such a case, since the casts happen before each FSDP
2025-03-14T04:33:23.6939008Z         instance's forward, a parent FSDP instance should have its non-FSDP
2025-03-14T04:33:23.6939243Z         submodules run before its FSDP submodules to avoid the activation dtype
2025-03-14T04:33:23.6939465Z         being changed due to a different ``MixedPrecision`` configuration.
2025-03-14T04:33:23.6939551Z     
2025-03-14T04:33:23.6939658Z         Example::
2025-03-14T04:33:23.6939744Z     
2025-03-14T04:33:23.6939892Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6940064Z             >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2025-03-14T04:33:23.6940176Z             >>> model[1] = FSDP(
2025-03-14T04:33:23.6940273Z             >>>     model[1],
2025-03-14T04:33:23.6940605Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2025-03-14T04:33:23.6940720Z             >>> )
2025-03-14T04:33:23.6940832Z             >>> model = FSDP(
2025-03-14T04:33:23.6940925Z             >>>     model,
2025-03-14T04:33:23.6941246Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2025-03-14T04:33:23.6941334Z             >>> )
2025-03-14T04:33:23.6941433Z     
2025-03-14T04:33:23.6941647Z         The above shows a working example. On the other hand, if ``model[1]``
2025-03-14T04:33:23.6941863Z         were replaced with ``model[0]``, meaning that the submodule using
2025-03-14T04:33:23.6942086Z         different ``MixedPrecision`` ran its forward first, then ``model[1]``
2025-03-14T04:33:23.6942318Z         would incorrectly see ``float16`` activations instead of ``bfloat16``
2025-03-14T04:33:23.6942408Z         ones.
2025-03-14T04:33:23.6942494Z     
2025-03-14T04:33:23.6942594Z     
2025-03-14T04:33:23.6942847Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6942943Z     
2025-03-14T04:33:23.6943047Z       warnings.warn(msg)
2025-03-14T04:33:23.6943141Z     
2025-03-14T04:33:23.6943344Z --- Parse Warning: 63 / 116 ---
2025-03-14T04:33:23.6944317Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295.
2025-03-14T04:33:23.6944581Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6944680Z     
2025-03-14T04:33:23.6944889Z     ``FullStateDictConfig`` is a config class meant to be used with
2025-03-14T04:33:23.6945102Z     ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
2025-03-14T04:33:23.6945309Z     ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
2025-03-14T04:33:23.6945547Z     dicts to save GPU memory and CPU memory, respectively. This config class
2025-03-14T04:33:23.6945752Z     is meant to be used via the :func:`state_dict_type` context manager as
2025-03-14T04:33:23.6945856Z     follows:
2025-03-14T04:33:23.6945947Z     
2025-03-14T04:33:23.6946097Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6946337Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:23.6946486Z         >>> fsdp = FSDP(model, auto_wrap_policy=...)
2025-03-14T04:33:23.6946692Z         >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
2025-03-14T04:33:23.6946930Z         >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
2025-03-14T04:33:23.6947045Z         >>>     state = fsdp.state_dict()
2025-03-14T04:33:23.6947273Z         >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
2025-03-14T04:33:23.6947565Z         >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
2025-03-14T04:33:23.6947811Z         >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
2025-03-14T04:33:23.6947921Z         >>> if dist.get_rank() == 0:
2025-03-14T04:33:23.6948116Z         >>> # Load checkpoint only on rank 0 to avoid memory redundancy
2025-03-14T04:33:23.6948263Z         >>>     state_dict = torch.load("my_checkpoint.pt")
2025-03-14T04:33:23.6948401Z         >>>     model.load_state_dict(state_dict)
2025-03-14T04:33:23.6948638Z         >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
2025-03-14T04:33:23.6949093Z         >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
2025-03-14T04:33:23.6949194Z         >>> fsdp = FSDP(
2025-03-14T04:33:23.6949301Z         ...     model,
2025-03-14T04:33:23.6949441Z         ...     device_id=torch.cuda.current_device(),
2025-03-14T04:33:23.6949568Z         ...     auto_wrap_policy=...,
2025-03-14T04:33:23.6949687Z         ...     sync_module_states=True,
2025-03-14T04:33:23.6949790Z         ... )
2025-03-14T04:33:23.6950007Z         >>> # After this point, all ranks have FSDP model with loaded checkpoint.
2025-03-14T04:33:23.6950192Z     
2025-03-14T04:33:23.6950289Z     Attributes:
2025-03-14T04:33:23.6950508Z         rank0_only (bool): If ``True``, then only rank 0 saves the full state
2025-03-14T04:33:23.6950716Z             dict, and nonzero ranks save an empty dict. If ``False``, then all
2025-03-14T04:33:23.6950897Z             ranks save the full state dict. (Default: ``False``)
2025-03-14T04:33:23.6950986Z     
2025-03-14T04:33:23.6951256Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6951343Z     
2025-03-14T04:33:23.6951461Z       warnings.warn(msg)
2025-03-14T04:33:23.6951546Z     
2025-03-14T04:33:23.6951769Z --- Parse Warning: 64 / 116 ---
2025-03-14T04:33:23.6952925Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=639.
2025-03-14T04:33:23.6953206Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6953456Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-14T04:33:23.6953597Z     
2025-03-14T04:33:23.6953863Z             Also takes (optional) configuration for the model's and optimizer's state dict.
2025-03-14T04:33:23.6954089Z             The target module does not have to be a FSDP module. If the target
2025-03-14T04:33:23.6954303Z             module is a FSDP module, its ``state_dict_type`` will also be changed.
2025-03-14T04:33:23.6954405Z     
2025-03-14T04:33:23.6954606Z             .. note:: This API should be called for only the top-level (root)
2025-03-14T04:33:23.6954718Z                 module.
2025-03-14T04:33:23.6954806Z     
2025-03-14T04:33:23.6955034Z             .. note:: This API enables users to transparently use the conventional
2025-03-14T04:33:23.6955233Z                 ``state_dict`` API to take model checkpoints in cases where the
2025-03-14T04:33:23.6955458Z                 root FSDP module is wrapped by another ``nn.Module``. For example,
2025-03-14T04:33:23.6955671Z                 the following will ensure ``state_dict`` is called on all non-FSDP
2025-03-14T04:33:23.6955924Z                 instances, while dispatching into `sharded_state_dict` implementation
2025-03-14T04:33:23.6956022Z                 for FSDP:
2025-03-14T04:33:23.6956124Z     
2025-03-14T04:33:23.6956218Z             Example::
2025-03-14T04:33:23.6956319Z     
2025-03-14T04:33:23.6956460Z                 >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6956587Z                 >>> model = DDP(FSDP(...))
2025-03-14T04:33:23.6956707Z                 >>> FSDP.set_state_dict_type(
2025-03-14T04:33:23.6956884Z                 >>>     model,
2025-03-14T04:33:23.6957024Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-14T04:33:23.6957261Z                 >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2025-03-14T04:33:23.6957490Z                 >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2025-03-14T04:33:23.6957594Z                 >>> )
2025-03-14T04:33:23.6957728Z                 >>> param_state_dict = model.state_dict()
2025-03-14T04:33:23.6957913Z                 >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-14T04:33:23.6957998Z     
2025-03-14T04:33:23.6958099Z             Args:
2025-03-14T04:33:23.6958228Z                 module (torch.nn.Module): Root module.
2025-03-14T04:33:23.6958474Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-14T04:33:23.6958712Z                 state_dict_config (Optional[StateDictConfig]): the configuration for the
2025-03-14T04:33:23.6958846Z                     target ``state_dict_type``.
2025-03-14T04:33:23.6959098Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2025-03-14T04:33:23.6959343Z                     for the optimizer state dict.
2025-03-14T04:33:23.6959428Z     
2025-03-14T04:33:23.6959519Z             Returns:
2025-03-14T04:33:23.6959757Z                 A StateDictSettings that include the previous state_dict type and
2025-03-14T04:33:23.6959879Z                 configuration for the module.
2025-03-14T04:33:23.6959983Z             
2025-03-14T04:33:23.6960237Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6960336Z     
2025-03-14T04:33:23.6960441Z       warnings.warn(msg)
2025-03-14T04:33:23.6960541Z     
2025-03-14T04:33:23.6960747Z --- Parse Warning: 65 / 116 ---
2025-03-14T04:33:23.6961910Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=797.
2025-03-14T04:33:23.6962182Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6962443Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-14T04:33:23.6962531Z     
2025-03-14T04:33:23.6962891Z             This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2025-03-14T04:33:23.6963036Z             :meth:`set_state_dict_type` for the detail.
2025-03-14T04:33:23.6963137Z     
2025-03-14T04:33:23.6963239Z             Example::
2025-03-14T04:33:23.6963339Z     
2025-03-14T04:33:23.6963479Z                 >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6963603Z                 >>> model = DDP(FSDP(...))
2025-03-14T04:33:23.6963722Z                 >>> with FSDP.state_dict_type(
2025-03-14T04:33:23.6963833Z                 >>>     model,
2025-03-14T04:33:23.6963972Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-14T04:33:23.6964077Z                 >>> ):
2025-03-14T04:33:23.6964206Z                 >>>     checkpoint = model.state_dict()
2025-03-14T04:33:23.6964305Z     
2025-03-14T04:33:23.6964392Z             Args:
2025-03-14T04:33:23.6964538Z                 module (torch.nn.Module): Root module.
2025-03-14T04:33:23.6964777Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-14T04:33:23.6965024Z                 state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2025-03-14T04:33:23.6965192Z                     configuration for the target ``state_dict_type``.
2025-03-14T04:33:23.6965440Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2025-03-14T04:33:23.6965640Z                    ``state_dict`` configuration for the target ``state_dict_type``.
2025-03-14T04:33:23.6965793Z             
2025-03-14T04:33:23.6966049Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6966151Z     
2025-03-14T04:33:23.6966255Z       warnings.warn(msg)
2025-03-14T04:33:23.6966354Z     
2025-03-14T04:33:23.6966545Z --- Parse Warning: 66 / 116 ---
2025-03-14T04:33:23.6967714Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1810.
2025-03-14T04:33:23.6967977Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6968079Z     
2025-03-14T04:33:23.6968319Z     Transform the state-dict of an optimizer corresponding to a sharded model.
2025-03-14T04:33:23.6968418Z     
2025-03-14T04:33:23.6968617Z     The given state-dict can be transformed to one of three types:
2025-03-14T04:33:23.6968931Z     1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2025-03-14T04:33:23.6969047Z     
2025-03-14T04:33:23.6969293Z     For full optimizer state_dict, all states are unflattened and not sharded.
2025-03-14T04:33:23.6969507Z     Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2025-03-14T04:33:23.6969611Z     avoid OOM.
2025-03-14T04:33:23.6969692Z     
2025-03-14T04:33:23.6969945Z     For sharded optimizer state_dict, all states are unflattened but sharded.
2025-03-14T04:33:23.6970150Z     CPU only can be specified via :meth:`state_dict_type` to further save
2025-03-14T04:33:23.6970252Z     memory.
2025-03-14T04:33:23.6970335Z     
2025-03-14T04:33:23.6970566Z     For local state_dict, no transformation will be performed. But a state
2025-03-14T04:33:23.6970805Z     will be converted from nn.Tensor to ShardedTensor to represent its sharding
2025-03-14T04:33:23.6970938Z     nature (this is not supported yet).
2025-03-14T04:33:23.6971024Z     
2025-03-14T04:33:23.6971129Z     Example::
2025-03-14T04:33:23.6971214Z     
2025-03-14T04:33:23.6971348Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6971600Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:23.6971761Z         >>> from torch.distributed.fsdp import StateDictType
2025-03-14T04:33:23.6972041Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-14T04:33:23.6972248Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-14T04:33:23.6972367Z         >>> # Save a checkpoint
2025-03-14T04:33:23.6972474Z         >>> model, optim = ...
2025-03-14T04:33:23.6972602Z         >>> FSDP.set_state_dict_type(
2025-03-14T04:33:23.6972693Z         >>>     model,
2025-03-14T04:33:23.6972832Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:23.6972968Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6973135Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6973222Z         >>> )
2025-03-14T04:33:23.6973353Z         >>> state_dict = model.state_dict()
2025-03-14T04:33:23.6973527Z         >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-14T04:33:23.6973691Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-14T04:33:23.6973793Z         >>> # Load a checkpoint
2025-03-14T04:33:23.6973911Z         >>> model, optim = ...
2025-03-14T04:33:23.6974069Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-14T04:33:23.6974196Z         >>> FSDP.set_state_dict_type(
2025-03-14T04:33:23.6974287Z         >>>     model,
2025-03-14T04:33:23.6974422Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:23.6974556Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6974716Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6974804Z         >>> )
2025-03-14T04:33:23.6974988Z         >>> model.load_state_dict(state_dict)
2025-03-14T04:33:23.6975144Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-14T04:33:23.6975276Z         >>>     model, optim, optim_state_dict
2025-03-14T04:33:23.6975364Z         >>> )
2025-03-14T04:33:23.6975504Z         >>> optim.load_state_dict(optim_state_dict)
2025-03-14T04:33:23.6975589Z     
2025-03-14T04:33:23.6975689Z     Args:
2025-03-14T04:33:23.6975892Z         model (torch.nn.Module): Root module (which may or may not be a
2025-03-14T04:33:23.6976112Z             :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-14T04:33:23.6976250Z             were passed into the optimizer ``optim``.
2025-03-14T04:33:23.6976448Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-14T04:33:23.6976546Z             parameters.
2025-03-14T04:33:23.6976773Z         optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2025-03-14T04:33:23.6976988Z             transform. If the value is None, optim.state_dict() will be used. (
2025-03-14T04:33:23.6977109Z             Default: ``None``)
2025-03-14T04:33:23.6977347Z         group (dist.ProcessGroup): Model's process group across which parameters
2025-03-14T04:33:23.6977581Z             are sharded or ``None`` if using the default process group. (
2025-03-14T04:33:23.6977684Z             Default: ``None``)
2025-03-14T04:33:23.6977784Z     
2025-03-14T04:33:23.6977876Z     Returns:
2025-03-14T04:33:23.6978085Z         Dict[str, Any]: A :class:`dict` containing the optimizer state for
2025-03-14T04:33:23.6978257Z         ``model``. The sharding of the optimizer state is based on
2025-03-14T04:33:23.6978359Z         ``state_dict_type``.
2025-03-14T04:33:23.6978455Z     
2025-03-14T04:33:23.6978709Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6978809Z     
2025-03-14T04:33:23.6978912Z       warnings.warn(msg)
2025-03-14T04:33:23.6979008Z     
2025-03-14T04:33:23.6979207Z --- Parse Warning: 67 / 116 ---
2025-03-14T04:33:23.6980409Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1908.
2025-03-14T04:33:23.6980677Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6980780Z     
2025-03-14T04:33:23.6981174Z     Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2025-03-14T04:33:23.6981279Z     
2025-03-14T04:33:23.6981452Z     Given a ``optim_state_dict`` that is transformed through
2025-03-14T04:33:23.6981683Z     :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2025-03-14T04:33:23.6981899Z     state_dict that can be loaded to ``optim`` which is the optimizer for
2025-03-14T04:33:23.6982106Z     ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2025-03-14T04:33:23.6982196Z     
2025-03-14T04:33:23.6982346Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.6982590Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-14T04:33:23.6982769Z         >>> from torch.distributed.fsdp import StateDictType
2025-03-14T04:33:23.6982952Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-14T04:33:23.6983173Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-14T04:33:23.6983279Z         >>> # Save a checkpoint
2025-03-14T04:33:23.6983398Z         >>> model, optim = ...
2025-03-14T04:33:23.6983514Z         >>> FSDP.set_state_dict_type(
2025-03-14T04:33:23.6983623Z         >>>     model,
2025-03-14T04:33:23.6983751Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:23.6983904Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6984080Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6984225Z         >>> )
2025-03-14T04:33:23.6984346Z         >>> state_dict = model.state_dict()
2025-03-14T04:33:23.6984489Z         >>> original_osd = optim.state_dict()
2025-03-14T04:33:23.6984627Z         >>> optim_state_dict = FSDP.optim_state_dict(
2025-03-14T04:33:23.6984735Z         >>>     model,
2025-03-14T04:33:23.6984829Z         >>>     optim,
2025-03-14T04:33:23.6984965Z         >>>     optim_state_dict=original_osd
2025-03-14T04:33:23.6985057Z         >>> )
2025-03-14T04:33:23.6985226Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-14T04:33:23.6985331Z         >>> # Load a checkpoint
2025-03-14T04:33:23.6985451Z         >>> model, optim = ...
2025-03-14T04:33:23.6985611Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-14T04:33:23.6985740Z         >>> FSDP.set_state_dict_type(
2025-03-14T04:33:23.6985832Z         >>>     model,
2025-03-14T04:33:23.6985973Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-14T04:33:23.6986113Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6986278Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-14T04:33:23.6986392Z         >>> )
2025-03-14T04:33:23.6986526Z         >>> model.load_state_dict(state_dict)
2025-03-14T04:33:23.6986680Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-14T04:33:23.6986812Z         >>>     model, optim, optim_state_dict
2025-03-14T04:33:23.6986898Z         >>> )
2025-03-14T04:33:23.6987028Z         >>> optim.load_state_dict(optim_state_dict)
2025-03-14T04:33:23.6987125Z     
2025-03-14T04:33:23.6987213Z     Args:
2025-03-14T04:33:23.6987421Z         model (torch.nn.Module): Root module (which may or may not be a
2025-03-14T04:33:23.6987624Z             :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-14T04:33:23.6987773Z             were passed into the optimizer ``optim``.
2025-03-14T04:33:23.6987958Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-14T04:33:23.6988070Z             parameters.
2025-03-14T04:33:23.6988283Z         optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2025-03-14T04:33:23.6988498Z         is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2025-03-14T04:33:23.6988694Z             KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2025-03-14T04:33:23.6988886Z             KeyedOptimizer or torch.distributed's NamedOptimizer.
2025-03-14T04:33:23.6989109Z         load_directly (bool): If this is set to True, this API will also
2025-03-14T04:33:23.6989328Z             call optim.load_state_dict(result) before returning the result.
2025-03-14T04:33:23.6989551Z             Otherwise, users are responsible to call ``optim.load_state_dict()``
2025-03-14T04:33:23.6989668Z             (Default: ``False``)
2025-03-14T04:33:23.6989907Z         group (dist.ProcessGroup): Model's process group across which parameters
2025-03-14T04:33:23.6990109Z             are sharded or ``None`` if using the default process group. (
2025-03-14T04:33:23.6990213Z             Default: ``None``)
2025-03-14T04:33:23.6990313Z     
2025-03-14T04:33:23.6990568Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.6990671Z     
2025-03-14T04:33:23.6990775Z       warnings.warn(msg)
2025-03-14T04:33:23.6990875Z     
2025-03-14T04:33:23.6991071Z --- Parse Warning: 68 / 116 ---
2025-03-14T04:33:23.6992071Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=128.
2025-03-14T04:33:23.6992335Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.6992434Z     
2025-03-14T04:33:23.6992662Z     RemoteModule instance can only be created after RPC initialization.
2025-03-14T04:33:23.6992760Z     
2025-03-14T04:33:23.6993011Z     It creates a user-specified module on a specified remote node.
2025-03-14T04:33:23.6993257Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-14T04:33:23.6993372Z     executed on the remote node.
2025-03-14T04:33:23.6993618Z     It takes care of autograd recording to ensure the backward pass propagates
2025-03-14T04:33:23.6993779Z     gradients back to the corresponding remote module.
2025-03-14T04:33:23.6994147Z     It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2025-03-14T04:33:23.6994351Z     without incurring any overheads of copying the actual module,
2025-03-14T04:33:23.6994568Z     which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2025-03-14T04:33:23.6994683Z     pointing to the remote module.
2025-03-14T04:33:23.6994783Z     
2025-03-14T04:33:23.6994987Z     The arguments of ``forward_async`` and ``forward`` are the same as
2025-03-14T04:33:23.6995201Z     the ``forward`` method of the module returned by the ``module_cls``.
2025-03-14T04:33:23.6995289Z     
2025-03-14T04:33:23.6995611Z     Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2025-03-14T04:33:23.6995724Z     
2025-03-14T04:33:23.6995988Z     Particularly, to create a hybrid model, typically the local modules should be
2025-03-14T04:33:23.6996359Z     created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2025-03-14T04:33:23.6996473Z     Hybrid Example:
2025-03-14T04:33:23.6996597Z             >>> class HybridModel(nn.Module):
2025-03-14T04:33:23.6996730Z             >>>     def __init__(self) -> None:
2025-03-14T04:33:23.6996850Z             >>>         nn.Module.__init__(self)
2025-03-14T04:33:23.6997010Z             >>>         self.remote_embedding = RemoteModule(...)
2025-03-14T04:33:23.6997142Z             >>>         self.local_linear = nn.Linear(...)
2025-03-14T04:33:23.6997242Z     
2025-03-14T04:33:23.6997448Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-14T04:33:23.6997712Z     that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2025-03-14T04:33:23.6997925Z     the generated ``RemoteModule`` will have 2 methods in signature of
2025-03-14T04:33:23.6998073Z     ``def forward(input: Tensor) -> Tensor:`` and
2025-03-14T04:33:23.6998242Z     ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2025-03-14T04:33:23.6998341Z     
2025-03-14T04:33:23.6998459Z     .. note::
2025-03-14T04:33:23.6998620Z         If the remote module is placed on a cuda device,
2025-03-14T04:33:23.6998856Z         any input CPU tensors will be automatically moved to the same cuda device,
2025-03-14T04:33:23.6999363Z         and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2025-03-14T04:33:23.6999453Z     
2025-03-14T04:33:23.6999556Z     Args:
2025-03-14T04:33:23.6999856Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-14T04:33:23.7000163Z             The device can be a local device or a remote device specified by one of the following remote
2025-03-14T04:33:23.7000260Z             formats:
2025-03-14T04:33:23.7000361Z     
2025-03-14T04:33:23.7000505Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-14T04:33:23.7000678Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-14T04:33:23.7000764Z     
2025-03-14T04:33:23.7001027Z             In addition, the device field can be optional and the default value is "cpu".
2025-03-14T04:33:23.7001151Z         module_cls (nn.Module): For example,
2025-03-14T04:33:23.7001281Z             >>> class MyModule(nn.Module):
2025-03-14T04:33:23.7001389Z             >>>     def forward(input):
2025-03-14T04:33:23.7001495Z             >>>         return input + 1
2025-03-14T04:33:23.7001595Z             >>>
2025-03-14T04:33:23.7001702Z             >>> module_cls = MyModule
2025-03-14T04:33:23.7001976Z         args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-14T04:33:23.7002171Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-14T04:33:23.7002463Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-14T04:33:23.7002698Z             to be created. The type object should be decorated by @torch.jit.interface.
2025-03-14T04:33:23.7002931Z             If not provided, the generated RemoteModule is not torchscript-able.
2025-03-14T04:33:23.7003168Z             Warning, this is an experimental API and susceptible to frequent changes.
2025-03-14T04:33:23.7003263Z     
2025-03-14T04:33:23.7003355Z     Returns:
2025-03-14T04:33:23.7003608Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-14T04:33:23.7003840Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-14T04:33:23.7004127Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-14T04:33:23.7004276Z         on the user-provided module on the remote side.
2025-03-14T04:33:23.7004401Z     
2025-03-14T04:33:23.7004498Z     Example::
2025-03-14T04:33:23.7004667Z         Run the following code in two different processes:
2025-03-14T04:33:23.7004754Z     
2025-03-14T04:33:23.7004886Z         >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7004987Z         >>> # On worker 0:
2025-03-14T04:33:23.7005098Z         >>> import torch
2025-03-14T04:33:23.7005232Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7005359Z         >>> from torch import nn, Tensor
2025-03-14T04:33:23.7005583Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-14T04:33:23.7005684Z         >>>
2025-03-14T04:33:23.7005829Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:23.7005967Z         >>> remote_linear_module = RemoteModule(
2025-03-14T04:33:23.7006100Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-14T04:33:23.7006204Z         >>> )
2025-03-14T04:33:23.7006316Z         >>> input = torch.randn(128, 20)
2025-03-14T04:33:23.7006491Z         >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-14T04:33:23.7006598Z         >>> ret = ret_fut.wait()
2025-03-14T04:33:23.7006716Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7006804Z     
2025-03-14T04:33:23.7006918Z         >>> # On worker 1:
2025-03-14T04:33:23.7007041Z         >>> import torch
2025-03-14T04:33:23.7007187Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7007275Z         >>>
2025-03-14T04:33:23.7007431Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:23.7007533Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7007618Z     
2025-03-14T04:33:23.7007888Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7007973Z     
2025-03-14T04:33:23.7008086Z       warnings.warn(msg)
2025-03-14T04:33:23.7008176Z     
2025-03-14T04:33:23.7008404Z --- Parse Warning: 69 / 116 ---
2025-03-14T04:33:23.7009448Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=505.
2025-03-14T04:33:23.7009730Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7009819Z     
2025-03-14T04:33:23.7010158Z     Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2025-03-14T04:33:23.7010246Z     
2025-03-14T04:33:23.7010584Z     This alternate initialization method can be particularly useful if we want to create multiple
2025-03-14T04:33:23.7010901Z     RemoteModule instances that share the same underlying module and reduce memory consumption.
2025-03-14T04:33:23.7011003Z     
2025-03-14T04:33:23.7011310Z     Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2025-03-14T04:33:23.7011534Z     which is not supported. The recommended way is as follows:
2025-03-14T04:33:23.7011623Z     
2025-03-14T04:33:23.7011766Z         1. the sender creates a RemoteModule;
2025-03-14T04:33:23.7011916Z         2. the sender sends its ``module_rref`` over RPC;
2025-03-14T04:33:23.7012267Z         3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2025-03-14T04:33:23.7012358Z     
2025-03-14T04:33:23.7012469Z     Example::
2025-03-14T04:33:23.7012626Z         Run the following code in two different processes:
2025-03-14T04:33:23.7012727Z     
2025-03-14T04:33:23.7012849Z         >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7012965Z         >>> # On worker 0:
2025-03-14T04:33:23.7013065Z         >>> import torch
2025-03-14T04:33:23.7013215Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7013330Z         >>> from torch import nn, Tensor
2025-03-14T04:33:23.7013575Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-14T04:33:23.7013666Z         >>>
2025-03-14T04:33:23.7013849Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:23.7013970Z         >>> remote_module = RemoteModule(
2025-03-14T04:33:23.7014116Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-14T04:33:23.7014209Z         >>> )
2025-03-14T04:33:23.7014312Z         >>>
2025-03-14T04:33:23.7014433Z         >>> remote_module1 = rpc.rpc_sync(
2025-03-14T04:33:23.7014553Z         >>>     "worker1/cpu",
2025-03-14T04:33:23.7014685Z         >>>     RemoteModule.init_from_module_rref,
2025-03-14T04:33:23.7014859Z         >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2025-03-14T04:33:23.7014950Z         >>> )
2025-03-14T04:33:23.7015070Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7015159Z     
2025-03-14T04:33:23.7015273Z         >>> # On worker 1:
2025-03-14T04:33:23.7015375Z         >>> import torch
2025-03-14T04:33:23.7015512Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7015617Z         >>>
2025-03-14T04:33:23.7015766Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:23.7015887Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7015975Z     
2025-03-14T04:33:23.7016083Z     Args:
2025-03-14T04:33:23.7016382Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-14T04:33:23.7016714Z             The device can be a local device or a remote device specified by one of the following remote
2025-03-14T04:33:23.7016806Z             formats:
2025-03-14T04:33:23.7016902Z     
2025-03-14T04:33:23.7017049Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-14T04:33:23.7017216Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-14T04:33:23.7017302Z     
2025-03-14T04:33:23.7017559Z             In addition, the device field can be optional and the default value is "cpu".
2025-03-14T04:33:23.7017812Z         module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2025-03-14T04:33:23.7017939Z             the created remote module.
2025-03-14T04:33:23.7018216Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-14T04:33:23.7018460Z             to be created. The type object should be decorated by @torch.jit.interface.
2025-03-14T04:33:23.7018684Z             If not provided, the generated RemoteModule is not torchscript-able.
2025-03-14T04:33:23.7018930Z             Warning, this is an experimental API and susceptible to frequent changes.
2025-03-14T04:33:23.7019019Z     
2025-03-14T04:33:23.7019124Z     Returns:
2025-03-14T04:33:23.7019369Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-14T04:33:23.7019617Z         user-provided ``module_rref``, it has a blocking ``forward`` method and an
2025-03-14T04:33:23.7019931Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-14T04:33:23.7020119Z         on the user-provided module on the remote side.
2025-03-14T04:33:23.7020206Z     
2025-03-14T04:33:23.7020473Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7020560Z     
2025-03-14T04:33:23.7020676Z       warnings.warn(msg)
2025-03-14T04:33:23.7020760Z     
2025-03-14T04:33:23.7020976Z --- Parse Warning: 70 / 116 ---
2025-03-14T04:33:23.7021941Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=597.
2025-03-14T04:33:23.7022216Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7022303Z     
2025-03-14T04:33:23.7022552Z         A RemoteModule instance can only be created after RPC initialization.
2025-03-14T04:33:23.7022643Z     
2025-03-14T04:33:23.7022857Z         It creates a user-specified module on a specified remote node.
2025-03-14T04:33:23.7023121Z         It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-14T04:33:23.7023247Z         executed on the remote node.
2025-03-14T04:33:23.7023486Z         It takes care of autograd recording to ensure the backward pass propagates
2025-03-14T04:33:23.7023661Z         gradients back to the corresponding remote module.
2025-03-14T04:33:23.7023748Z     
2025-03-14T04:33:23.7023979Z         It generates two methods ``forward_async`` and ``forward`` based on the
2025-03-14T04:33:23.7024198Z         signature of the ``forward`` method of ``module_cls``. ``forward_async``
2025-03-14T04:33:23.7024461Z         runs asynchronously and returns a Future. The arguments of ``forward_async``
2025-03-14T04:33:23.7024661Z         and ``forward`` are the same as the ``forward`` method of the module
2025-03-14T04:33:23.7024790Z         returned by the ``module_cls``.
2025-03-14T04:33:23.7024879Z     
2025-03-14T04:33:23.7025092Z         For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-14T04:33:23.7025347Z         that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2025-03-14T04:33:23.7025585Z         the generated ``RemoteModule`` will have 2 methods with the signatures:
2025-03-14T04:33:23.7025671Z     
2025-03-14T04:33:23.7025838Z         | ``def forward(input: Tensor) -> Tensor:``
2025-03-14T04:33:23.7026007Z         | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2025-03-14T04:33:23.7026106Z     
2025-03-14T04:33:23.7026196Z     Args:
2025-03-14T04:33:23.7026505Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-14T04:33:23.7026848Z             The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2025-03-14T04:33:23.7027013Z             E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2025-03-14T04:33:23.7027262Z             In addition, the device field can be optional and the default value is "cpu".
2025-03-14T04:33:23.7027526Z         module_cls (nn.Module): Class for the module to be created remotely. For example,
2025-03-14T04:33:23.7027612Z     
2025-03-14T04:33:23.7027741Z             >>> class MyModule(nn.Module):
2025-03-14T04:33:23.7027854Z             >>>     def forward(input):
2025-03-14T04:33:23.7027974Z             >>>         return input + 1
2025-03-14T04:33:23.7028066Z             >>>
2025-03-14T04:33:23.7028185Z             >>> module_cls = MyModule
2025-03-14T04:33:23.7028270Z     
2025-03-14T04:33:23.7028484Z         args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-14T04:33:23.7028676Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-14T04:33:23.7028761Z     
2025-03-14T04:33:23.7028863Z     Returns:
2025-03-14T04:33:23.7029107Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-14T04:33:23.7029404Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-14T04:33:23.7029679Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-14T04:33:23.7029839Z         on the user-provided module on the remote side.
2025-03-14T04:33:23.7029924Z     
2025-03-14T04:33:23.7030031Z     Example::
2025-03-14T04:33:23.7030189Z         Run the following code in two different processes:
2025-03-14T04:33:23.7030288Z     
2025-03-14T04:33:23.7030410Z         >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7030521Z         >>> # On worker 0:
2025-03-14T04:33:23.7030619Z         >>> import torch
2025-03-14T04:33:23.7030761Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7030874Z         >>> from torch import nn, Tensor
2025-03-14T04:33:23.7031112Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-14T04:33:23.7031200Z         >>>
2025-03-14T04:33:23.7031364Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:23.7031490Z         >>> remote_linear_module = RemoteModule(
2025-03-14T04:33:23.7031662Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-14T04:33:23.7031749Z         >>> )
2025-03-14T04:33:23.7031875Z         >>> input = torch.randn(128, 20)
2025-03-14T04:33:23.7032035Z         >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-14T04:33:23.7032150Z         >>> ret = ret_fut.wait()
2025-03-14T04:33:23.7032253Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7032352Z     
2025-03-14T04:33:23.7032449Z         >>> # On worker 1:
2025-03-14T04:33:23.7032559Z         >>> import torch
2025-03-14T04:33:23.7032689Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7032790Z         >>>
2025-03-14T04:33:23.7032933Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:23.7033047Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7033132Z     
2025-03-14T04:33:23.7033345Z         Furthermore, a more practical example that is combined with
2025-03-14T04:33:23.7033818Z         `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2025-03-14T04:33:23.7034161Z         can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2025-03-14T04:33:23.7034248Z     
2025-03-14T04:33:23.7034513Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7034626Z     
2025-03-14T04:33:23.7034729Z       warnings.warn(msg)
2025-03-14T04:33:23.7034826Z     
2025-03-14T04:33:23.7035026Z --- Parse Warning: 71 / 116 ---
2025-03-14T04:33:23.7036023Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130.
2025-03-14T04:33:23.7036303Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7036391Z     
2025-03-14T04:33:23.7036651Z     DistributedOptimizer takes remote references to parameters scattered
2025-03-14T04:33:23.7036899Z     across workers and applies the given optimizer locally for each parameter.
2025-03-14T04:33:23.7036984Z     
2025-03-14T04:33:23.7037236Z     This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2025-03-14T04:33:23.7037397Z     to retrieve the gradients for specific parameters.
2025-03-14T04:33:23.7037499Z     
2025-03-14T04:33:23.7037600Z     Concurrent calls to
2025-03-14T04:33:23.7037823Z     :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2025-03-14T04:33:23.7037971Z     either from the same or different clients, will
2025-03-14T04:33:23.7038209Z     be serialized on each worker -- as each worker's optimizer can only work
2025-03-14T04:33:23.7038418Z     on one set of gradients at a time. However, there is no guarantee that
2025-03-14T04:33:23.7038731Z     the full forward-backward-optimizer sequence will execute for one client
2025-03-14T04:33:23.7038952Z     at a time. This means that the gradients being applied may not correspond
2025-03-14T04:33:23.7039275Z     to the latest forward pass executed on a given worker. Also, there is no
2025-03-14T04:33:23.7039397Z     guaranteed ordering across workers.
2025-03-14T04:33:23.7039500Z     
2025-03-14T04:33:23.7039765Z     `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2025-03-14T04:33:23.7040011Z     by default, so that optimizer updates are not blocked by the Python Global
2025-03-14T04:33:23.7040256Z     Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2025-03-14T04:33:23.7040510Z     Model Parallel). This feature is currently enabled for most optimizers. You
2025-03-14T04:33:23.7040763Z     can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2025-03-14T04:33:23.7040894Z     for your own custom optimizers.
2025-03-14T04:33:23.7040990Z     
2025-03-14T04:33:23.7041094Z     Args:
2025-03-14T04:33:23.7041294Z         optimizer_class (optim.Optimizer): the class of optimizer to
2025-03-14T04:33:23.7041455Z             instantiate on each worker.
2025-03-14T04:33:23.7041667Z         params_rref (list[RRef]): list of RRefs to local or remote parameters
2025-03-14T04:33:23.7041786Z             to optimize.
2025-03-14T04:33:23.7042004Z         args: arguments to pass to the optimizer constructor on each worker.
2025-03-14T04:33:23.7042245Z         kwargs: arguments to pass to the optimizer constructor on each worker.
2025-03-14T04:33:23.7042336Z     
2025-03-14T04:33:23.7042448Z     Example::
2025-03-14T04:33:23.7042571Z         >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7042758Z         >>> import torch.distributed.autograd as dist_autograd
2025-03-14T04:33:23.7042891Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7043016Z         >>> from torch import optim
2025-03-14T04:33:23.7043215Z         >>> from torch.distributed.optim import DistributedOptimizer
2025-03-14T04:33:23.7043320Z         >>>
2025-03-14T04:33:23.7043463Z         >>> with dist_autograd.context() as context_id:
2025-03-14T04:33:23.7043588Z         >>>   # Forward pass.
2025-03-14T04:33:23.7043794Z         >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2025-03-14T04:33:23.7044008Z         >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2025-03-14T04:33:23.7044171Z         >>>   loss = rref1.to_here() + rref2.to_here()
2025-03-14T04:33:23.7044276Z         >>>
2025-03-14T04:33:23.7044383Z         >>>   # Backward pass.
2025-03-14T04:33:23.7044555Z         >>>   dist_autograd.backward(context_id, [loss.sum()])
2025-03-14T04:33:23.7044646Z         >>>
2025-03-14T04:33:23.7044761Z         >>>   # Optimizer.
2025-03-14T04:33:23.7044892Z         >>>   dist_optim = DistributedOptimizer(
2025-03-14T04:33:23.7045007Z         >>>      optim.SGD,
2025-03-14T04:33:23.7045114Z         >>>      [rref1, rref2],
2025-03-14T04:33:23.7045229Z         >>>      lr=0.05,
2025-03-14T04:33:23.7045321Z         >>>   )
2025-03-14T04:33:23.7045439Z         >>>   dist_optim.step(context_id)
2025-03-14T04:33:23.7045543Z     
2025-03-14T04:33:23.7045708Z     __ https://github.com/pytorch/tutorials/pull/1465
2025-03-14T04:33:23.7045812Z     
2025-03-14T04:33:23.7046067Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7046172Z     
2025-03-14T04:33:23.7046279Z       warnings.warn(msg)
2025-03-14T04:33:23.7046382Z     
2025-03-14T04:33:23.7046589Z --- Parse Warning: 72 / 116 ---
2025-03-14T04:33:23.7047650Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9.
2025-03-14T04:33:23.7047941Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7048069Z     
2025-03-14T04:33:23.7048459Z     Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-14T04:33:23.7048632Z     This optimizer runs local optimizer at every step.
2025-03-14T04:33:23.7049118Z     After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.
2025-03-14T04:33:23.7049219Z     
2025-03-14T04:33:23.7049308Z     Args:
2025-03-14T04:33:23.7049438Z         optim: The local optimizer.
2025-03-14T04:33:23.7049659Z         averager: A model averager instance to run post-localSGD algorithm.
2025-03-14T04:33:23.7049759Z     
2025-03-14T04:33:23.7049855Z     Example::
2025-03-14T04:33:23.7049955Z     
2025-03-14T04:33:23.7050087Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.7050202Z         >>> import torch
2025-03-14T04:33:23.7050326Z         >>> import torch.distributed as dist
2025-03-14T04:33:23.7050616Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-14T04:33:23.7050725Z         >>> import torch.nn as nn
2025-03-14T04:33:23.7051006Z         >>> from torch.distributed.optim import PostLocalSGDOptimizer
2025-03-14T04:33:23.7051279Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-14T04:33:23.7051402Z         >>>   PostLocalSGDState,
2025-03-14T04:33:23.7051508Z         >>>   post_localSGD_hook,
2025-03-14T04:33:23.7051612Z         >>> )
2025-03-14T04:33:23.7051700Z         >>>
2025-03-14T04:33:23.7051876Z         >>> model = nn.parallel.DistributedDataParallel(
2025-03-14T04:33:23.7052024Z         >>>    module, device_ids=[rank], output_device=rank
2025-03-14T04:33:23.7052127Z         >>> )
2025-03-14T04:33:23.7052217Z         >>>
2025-03-14T04:33:23.7052382Z         >>> # Register a post-localSGD communication hook.
2025-03-14T04:33:23.7052681Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-14T04:33:23.7052864Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-14T04:33:23.7052952Z         >>>
2025-03-14T04:33:23.7053174Z         >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2025-03-14T04:33:23.7053426Z         >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2025-03-14T04:33:23.7053608Z         >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-14T04:33:23.7053872Z         >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2025-03-14T04:33:23.7054008Z         >>> opt = PostLocalSGDOptimizer(
2025-03-14T04:33:23.7054116Z         >>>     optim=local_optim,
2025-03-14T04:33:23.7054378Z         >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-14T04:33:23.7054466Z         >>> )
2025-03-14T04:33:23.7054566Z         >>>
2025-03-14T04:33:23.7054795Z         >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2025-03-14T04:33:23.7055111Z         >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2025-03-14T04:33:23.7055493Z         >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2025-03-14T04:33:23.7055621Z         >>> for step in range(0, 200):
2025-03-14T04:33:23.7055727Z         >>>    opt.zero_grad()
2025-03-14T04:33:23.7055866Z         >>>    loss = loss_fn(output, labels)
2025-03-14T04:33:23.7055973Z         >>>    loss.backward()
2025-03-14T04:33:23.7056074Z         >>>    opt.step()
2025-03-14T04:33:23.7056175Z     
2025-03-14T04:33:23.7056432Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7056533Z     
2025-03-14T04:33:23.7056637Z       warnings.warn(msg)
2025-03-14T04:33:23.7056736Z     
2025-03-14T04:33:23.7056950Z --- Parse Warning: 73 / 116 ---
2025-03-14T04:33:23.7058069Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284.
2025-03-14T04:33:23.7058368Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7058467Z     
2025-03-14T04:33:23.7058874Z     Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2025-03-14T04:33:23.7058978Z     
2025-03-14T04:33:23.7059111Z     The sharing is done as described by ZeRO_.
2025-03-14T04:33:23.7059209Z     
2025-03-14T04:33:23.7059360Z     The local optimizer instance in each rank is only
2025-03-14T04:33:23.7059622Z     responsible for updating approximately ``1 / world_size`` parameters and
2025-03-14T04:33:23.7059833Z     hence only needs to keep ``1 / world_size`` optimizer states. After
2025-03-14T04:33:23.7060096Z     parameters are updated locally, each rank will broadcast its parameters to
2025-03-14T04:33:23.7060287Z     all other peers to keep all model replicas in the same state.
2025-03-14T04:33:23.7060526Z     ``ZeroRedundancyOptimizer`` can be used in conjunction with
2025-03-14T04:33:23.7060787Z     :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2025-03-14T04:33:23.7060904Z     memory consumption.
2025-03-14T04:33:23.7060990Z     
2025-03-14T04:33:23.7061263Z     ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2025-03-14T04:33:23.7061496Z     of parameters at each rank. Each parameter belongs to a single rank and is
2025-03-14T04:33:23.7061748Z     not divided among ranks. The partition is arbitrary and might not match the
2025-03-14T04:33:23.7061883Z     the parameter registration or usage order.
2025-03-14T04:33:23.7061986Z     
2025-03-14T04:33:23.7062079Z     Arguments:
2025-03-14T04:33:23.7062289Z         params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2025-03-14T04:33:23.7062483Z             or :class:`dict` s giving all parameters, which will be sharded
2025-03-14T04:33:23.7062601Z             across ranks.
2025-03-14T04:33:23.7062689Z     
2025-03-14T04:33:23.7062799Z     Keyword Args:
2025-03-14T04:33:23.7063028Z         optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2025-03-14T04:33:23.7063136Z             optimizer.
2025-03-14T04:33:23.7063347Z         process_group (``ProcessGroup``, optional): ``torch.distributed``
2025-03-14T04:33:23.7063585Z             ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2025-03-14T04:33:23.7063739Z             :meth:`torch.distributed.init_process_group`).
2025-03-14T04:33:23.7063981Z         parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2025-03-14T04:33:23.7064194Z             packed into buckets to speed up communication, and ``param.data``
2025-03-14T04:33:23.7064409Z             fields point to bucket views at different offsets; if ``False``,
2025-03-14T04:33:23.7064621Z             each individual parameter is communicated separately, and each
2025-03-14T04:33:23.7064789Z             ``params.data`` stays intact (default: ``False``).
2025-03-14T04:33:23.7064986Z         overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2025-03-14T04:33:23.7065199Z             overlapped with :class:`DistributedDataParallel` 's gradient
2025-03-14T04:33:23.7065416Z             synchronization; this requires (1) either a functional optimizer
2025-03-14T04:33:23.7065615Z             for the ``optimizer_class`` argument or one with a functional
2025-03-14T04:33:23.7065794Z             equivalent and (2) registering a DDP communication hook
2025-03-14T04:33:23.7066010Z             constructed from one of the functions in ``ddp_zero_hook.py``;
2025-03-14T04:33:23.7066183Z             parameters are packed into buckets matching those in
2025-03-14T04:33:23.7066361Z             :class:`DistributedDataParallel`, meaning that the
2025-03-14T04:33:23.7066540Z             ``parameters_as_bucket_view`` argument is ignored.
2025-03-14T04:33:23.7066766Z             If ``False``, :meth:`step` runs disjointly after the backward pass
2025-03-14T04:33:23.7066869Z             (per normal).
2025-03-14T04:33:23.7066987Z             (default: ``False``)
2025-03-14T04:33:23.7067202Z         **defaults: any trailing arguments, which are forwarded to the local
2025-03-14T04:33:23.7067310Z             optimizer.
2025-03-14T04:33:23.7067396Z     
2025-03-14T04:33:23.7067510Z     Example::
2025-03-14T04:33:23.7067597Z     
2025-03-14T04:33:23.7067717Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7067825Z         >>> import torch.nn as nn
2025-03-14T04:33:23.7068052Z         >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2025-03-14T04:33:23.7068256Z         >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-14T04:33:23.7068496Z         >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2025-03-14T04:33:23.7068623Z         >>> ddp = DDP(model, device_ids=[rank])
2025-03-14T04:33:23.7068764Z         >>> opt = ZeroRedundancyOptimizer(
2025-03-14T04:33:23.7068873Z         >>>     ddp.parameters(),
2025-03-14T04:33:23.7069043Z         >>>     optimizer_class=torch.optim.Adam,
2025-03-14T04:33:23.7069135Z         >>>     lr=0.01
2025-03-14T04:33:23.7069224Z         >>> )
2025-03-14T04:33:23.7069351Z         >>> ddp(inputs).sum().backward()
2025-03-14T04:33:23.7075160Z         >>> opt.step()
2025-03-14T04:33:23.7075305Z     
2025-03-14T04:33:23.7075429Z     .. warning::
2025-03-14T04:33:23.7075679Z         Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2025-03-14T04:33:23.7075837Z         passed-in parameters are the same dense type.
2025-03-14T04:33:23.7075940Z     
2025-03-14T04:33:23.7076036Z     .. warning::
2025-03-14T04:33:23.7076271Z         If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2025-03-14T04:33:23.7076480Z         the way that overlapping :class:`DistributedDataParallel` with
2025-03-14T04:33:23.7076725Z         :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2025-03-14T04:33:23.7076958Z         two or three training iterations do not perform parameter updates in
2025-03-14T04:33:23.7077162Z         the optimizer step, depending on if ``static_graph=False`` or
2025-03-14T04:33:23.7077363Z         ``static_graph=True``, respectively. This is because it needs
2025-03-14T04:33:23.7077553Z         information about the gradient bucketing strategy used by
2025-03-14T04:33:23.7077873Z         :class:`DistributedDataParallel`, which is not finalized until the
2025-03-14T04:33:23.7078079Z         second forward pass if ``static_graph=False`` or until the third
2025-03-14T04:33:23.7078302Z         forward pass if ``static_graph=True``. To adjust for this, one option
2025-03-14T04:33:23.7078416Z         is to prepend dummy inputs.
2025-03-14T04:33:23.7078521Z     
2025-03-14T04:33:23.7078782Z     .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2025-03-14T04:33:23.7078886Z     
2025-03-14T04:33:23.7079027Z     .. _ZeRO: https://arxiv.org/abs/1910.02054
2025-03-14T04:33:23.7079216Z     
2025-03-14T04:33:23.7079307Z     
2025-03-14T04:33:23.7079576Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7079663Z     
2025-03-14T04:33:23.7079769Z       warnings.warn(msg)
2025-03-14T04:33:23.7079872Z     
2025-03-14T04:33:23.7080120Z --- Parse Warning: 74 / 116 ---
2025-03-14T04:33:23.7081109Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28.
2025-03-14T04:33:23.7081387Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7081475Z     
2025-03-14T04:33:23.7081705Z     Custom reducer class that can be used to specify a custom operation that
2025-03-14T04:33:23.7081971Z     reduces losses of multiple microbatches into one value.
2025-03-14T04:33:23.7082057Z     
2025-03-14T04:33:23.7082165Z     Example:
2025-03-14T04:33:23.7082270Z     >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7082402Z     >>> sum_reducer = _CustomReducer(
2025-03-14T04:33:23.7082506Z     >>>     torch.tensor(0.0),
2025-03-14T04:33:23.7082621Z     >>>     lambda a, b: a + b
2025-03-14T04:33:23.7082710Z     >>> )
2025-03-14T04:33:23.7082811Z     
2025-03-14T04:33:23.7083068Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7083166Z     
2025-03-14T04:33:23.7083269Z       warnings.warn(msg)
2025-03-14T04:33:23.7083369Z     
2025-03-14T04:33:23.7083561Z --- Parse Warning: 75 / 116 ---
2025-03-14T04:33:23.7084487Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6.
2025-03-14T04:33:23.7084754Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7084885Z     
2025-03-14T04:33:23.7085127Z     A decorator for a function indicating that the return value of the function
2025-03-14T04:33:23.7085353Z     is guaranteed to be a :class:`~torch.futures.Future` object and this
2025-03-14T04:33:23.7085597Z     function can run asynchronously on the RPC callee. More specifically, the
2025-03-14T04:33:23.7085854Z     callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2025-03-14T04:33:23.7086090Z     function and installs subsequent processing steps as a callback to that
2025-03-14T04:33:23.7086340Z     :class:`~torch.futures.Future`. The installed callback will read the value
2025-03-14T04:33:23.7086544Z     from the :class:`~torch.futures.Future` when completed and send the
2025-03-14T04:33:23.7086746Z     value back as the RPC response. That also means the returned
2025-03-14T04:33:23.7086982Z     :class:`~torch.futures.Future` only exists on the callee side and is never
2025-03-14T04:33:23.7087222Z     sent through RPC. This decorator is useful when the wrapped function's
2025-03-14T04:33:23.7087426Z     (``fn``) execution needs to pause and resume due to, e.g., containing
2025-03-14T04:33:23.7087668Z     :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2025-03-14T04:33:23.7087755Z     
2025-03-14T04:33:23.7088010Z     .. note:: To enable asynchronous execution, applications must pass the
2025-03-14T04:33:23.7088246Z         function object returned by this decorator to RPC APIs. If RPC detected
2025-03-14T04:33:23.7088480Z         attributes installed by this decorator, it knows that this function
2025-03-14T04:33:23.7088668Z         returns a ``Future`` object and will handle that accordingly.
2025-03-14T04:33:23.7088896Z         However, this does not mean this decorator has to be outmost one when
2025-03-14T04:33:23.7089126Z         defining a function. For example, when combined with ``@staticmethod``
2025-03-14T04:33:23.7089353Z         or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2025-03-14T04:33:23.7089579Z         inner decorator to allow the target function be recognized as a static
2025-03-14T04:33:23.7089821Z         or class function. This target function can still execute asynchronously
2025-03-14T04:33:23.7090052Z         because, when accessed, the static or class method preserves attributes
2025-03-14T04:33:23.7090226Z         installed by ``@rpc.functions.async_execution``.
2025-03-14T04:33:23.7090314Z     
2025-03-14T04:33:23.7090414Z     
2025-03-14T04:33:23.7090510Z     Example::
2025-03-14T04:33:23.7090725Z         The returned :class:`~torch.futures.Future` object can come from
2025-03-14T04:33:23.7090865Z         :meth:`~torch.distributed.rpc.rpc_async`,
2025-03-14T04:33:23.7091105Z         :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2025-03-14T04:33:23.7091311Z         constructor. The example below shows directly using the
2025-03-14T04:33:23.7091485Z         :class:`~torch.futures.Future` returned by
2025-03-14T04:33:23.7091613Z         :meth:`~torch.futures.Future.then`.
2025-03-14T04:33:23.7091716Z     
2025-03-14T04:33:23.7091841Z         >>> from torch.distributed import rpc
2025-03-14T04:33:23.7091942Z         >>>
2025-03-14T04:33:23.7092064Z         >>> # omitting setup and shutdown RPC
2025-03-14T04:33:23.7092164Z         >>>
2025-03-14T04:33:23.7092266Z         >>> # On all workers
2025-03-14T04:33:23.7092402Z         >>> @rpc.functions.async_execution
2025-03-14T04:33:23.7092527Z         >>> def async_add_chained(to, x, y, z):
2025-03-14T04:33:23.7092736Z         >>>     # This function runs on "worker1" and returns immediately when
2025-03-14T04:33:23.7092929Z         >>>     # the callback is installed through the `then(cb)` API. In the
2025-03-14T04:33:23.7093127Z         >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2025-03-14T04:33:23.7093294Z         >>>     # When the return value of that `rpc_async` arrives at
2025-03-14T04:33:23.7093501Z         >>>     # "worker1", "worker1" will run the lambda function accordingly
2025-03-14T04:33:23.7093733Z         >>>     # and set the value for the previously returned `Future`, which
2025-03-14T04:33:23.7093933Z         >>>     # will then trigger RPC to send the result back to "worker0".
2025-03-14T04:33:23.7094106Z         >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:23.7094238Z         >>>         lambda fut: fut.wait() + z
2025-03-14T04:33:23.7094329Z         >>>     )
2025-03-14T04:33:23.7094431Z         >>>
2025-03-14T04:33:23.7094528Z         >>> # On worker0
2025-03-14T04:33:23.7094646Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7094751Z         >>> ret = rpc.rpc_sync(
2025-03-14T04:33:23.7094852Z         >>>     "worker1",
2025-03-14T04:33:23.7094974Z         >>>     async_add_chained,
2025-03-14T04:33:23.7095102Z         >>>     args=("worker2", torch.ones(2), 1, 1)
2025-03-14T04:33:23.7095213Z         >>> )
2025-03-14T04:33:23.7095340Z         >>> print(ret)  # prints tensor([3., 3.])
2025-03-14T04:33:23.7095444Z     
2025-03-14T04:33:23.7095674Z         When combined with TorchScript decorators, this decorator must be the
2025-03-14T04:33:23.7095784Z         outmost one.
2025-03-14T04:33:23.7095873Z     
2025-03-14T04:33:23.7095994Z         >>> from torch import Tensor
2025-03-14T04:33:23.7096116Z         >>> from torch.futures import Future
2025-03-14T04:33:23.7096278Z         >>> from torch.distributed import rpc
2025-03-14T04:33:23.7096369Z         >>>
2025-03-14T04:33:23.7096500Z         >>> # omitting setup and shutdown RPC
2025-03-14T04:33:23.7096588Z         >>>
2025-03-14T04:33:23.7096701Z         >>> # On all workers
2025-03-14T04:33:23.7096805Z         >>> @torch.jit.script
2025-03-14T04:33:23.7096967Z         >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2025-03-14T04:33:23.7097067Z         >>>     return x + y
2025-03-14T04:33:23.7097166Z         >>>
2025-03-14T04:33:23.7097292Z         >>> @rpc.functions.async_execution
2025-03-14T04:33:23.7097407Z         >>> @torch.jit.script
2025-03-14T04:33:23.7097598Z         >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2025-03-14T04:33:23.7097759Z         >>>     return rpc.rpc_async(to, script_add, (x, y))
2025-03-14T04:33:23.7097847Z         >>>
2025-03-14T04:33:23.7097955Z         >>> # On worker0
2025-03-14T04:33:23.7098059Z         >>> ret = rpc.rpc_sync(
2025-03-14T04:33:23.7098168Z         >>>     "worker1",
2025-03-14T04:33:23.7098266Z         >>>     async_add,
2025-03-14T04:33:23.7098403Z         >>>     args=("worker2", torch.ones(2), 1)
2025-03-14T04:33:23.7098493Z         >>> )
2025-03-14T04:33:23.7098618Z         >>> print(ret)  # prints tensor([2., 2.])
2025-03-14T04:33:23.7098718Z     
2025-03-14T04:33:23.7098942Z         When combined with static or class method, this decorator must be the
2025-03-14T04:33:23.7099049Z         inner one.
2025-03-14T04:33:23.7099133Z     
2025-03-14T04:33:23.7099324Z         >>> from torch.distributed import rpc
2025-03-14T04:33:23.7099413Z         >>>
2025-03-14T04:33:23.7099547Z         >>> # omitting setup and shutdown RPC
2025-03-14T04:33:23.7099635Z         >>>
2025-03-14T04:33:23.7099746Z         >>> # On all workers
2025-03-14T04:33:23.7099866Z         >>> class AsyncExecutionClass:
2025-03-14T04:33:23.7099973Z         >>>
2025-03-14T04:33:23.7100073Z         >>>     @staticmethod
2025-03-14T04:33:23.7100216Z         >>>     @rpc.functions.async_execution
2025-03-14T04:33:23.7100336Z         >>>     def static_async_add(to, x, y, z):
2025-03-14T04:33:23.7100527Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:23.7100646Z         >>>             lambda fut: fut.wait() + z
2025-03-14T04:33:23.7100752Z         >>>         )
2025-03-14T04:33:23.7100839Z         >>>
2025-03-14T04:33:23.7100949Z         >>>     @classmethod
2025-03-14T04:33:23.7101073Z         >>>     @rpc.functions.async_execution
2025-03-14T04:33:23.7101217Z         >>>     def class_async_add(cls, to, x, y, z):
2025-03-14T04:33:23.7101347Z         >>>         ret_fut = torch.futures.Future()
2025-03-14T04:33:23.7101541Z         >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:23.7101697Z         >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2025-03-14T04:33:23.7101799Z         >>>         )
2025-03-14T04:33:23.7101904Z         >>>         return ret_fut
2025-03-14T04:33:23.7102007Z         >>>
2025-03-14T04:33:23.7102133Z         >>>     @rpc.functions.async_execution
2025-03-14T04:33:23.7102274Z         >>>     def bound_async_add(self, to, x, y, z):
2025-03-14T04:33:23.7102445Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-14T04:33:23.7102576Z         >>>             lambda fut: fut.wait() + z
2025-03-14T04:33:23.7102667Z         >>>         )
2025-03-14T04:33:23.7102753Z         >>>
2025-03-14T04:33:23.7102869Z         >>> # On worker0
2025-03-14T04:33:23.7102976Z         >>> ret = rpc.rpc_sync(
2025-03-14T04:33:23.7103089Z         >>>     "worker1",
2025-03-14T04:33:23.7103228Z         >>>     AsyncExecutionClass.static_async_add,
2025-03-14T04:33:23.7103371Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-14T04:33:23.7103460Z         >>> )
2025-03-14T04:33:23.7103597Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:23.7103682Z         >>>
2025-03-14T04:33:23.7103803Z         >>> ret = rpc.rpc_sync(
2025-03-14T04:33:23.7103925Z         >>>     "worker1",
2025-03-14T04:33:23.7104079Z         >>>     AsyncExecutionClass.class_async_add,
2025-03-14T04:33:23.7104203Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-14T04:33:23.7104306Z         >>> )
2025-03-14T04:33:23.7104430Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:23.7104531Z     
2025-03-14T04:33:23.7104696Z         This decorator also works with RRef helpers, i.e., .
2025-03-14T04:33:23.7104860Z         :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2025-03-14T04:33:23.7105027Z         :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2025-03-14T04:33:23.7105185Z         :meth:`torch.distributed.rpc.RRef.remote`.
2025-03-14T04:33:23.7105273Z     
2025-03-14T04:33:23.7105414Z         >>> from torch.distributed import rpc
2025-03-14T04:33:23.7105503Z         >>>
2025-03-14T04:33:23.7105657Z         >>> # reuse the AsyncExecutionClass class above
2025-03-14T04:33:23.7105814Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-14T04:33:23.7106044Z         >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2025-03-14T04:33:23.7106167Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:23.7106269Z         >>>
2025-03-14T04:33:23.7106426Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-14T04:33:23.7106680Z         >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2025-03-14T04:33:23.7106804Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:23.7106963Z         >>>
2025-03-14T04:33:23.7107120Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-14T04:33:23.7107370Z         >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2025-03-14T04:33:23.7107492Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-14T04:33:23.7107589Z     
2025-03-14T04:33:23.7107843Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7107942Z     
2025-03-14T04:33:23.7108046Z       warnings.warn(msg)
2025-03-14T04:33:23.7108132Z     
2025-03-14T04:33:23.7108364Z --- Parse Warning: 76 / 116 ---
2025-03-14T04:33:23.7109421Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108.
2025-03-14T04:33:23.7109700Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7109802Z     
2025-03-14T04:33:23.7110009Z     Set device mapping between each RPC caller and callee pair. This
2025-03-14T04:33:23.7110237Z     function can be called multiple times to incrementally add
2025-03-14T04:33:23.7110356Z     device placement configurations.
2025-03-14T04:33:23.7110441Z     
2025-03-14T04:33:23.7110544Z     Args:
2025-03-14T04:33:23.7110646Z         to (str): Callee name.
2025-03-14T04:33:23.7110863Z         device_map (Dict of int, str, or torch.device): Device placement
2025-03-14T04:33:23.7111048Z             mappings from this worker to the callee. This map must be
2025-03-14T04:33:23.7111160Z             invertible.
2025-03-14T04:33:23.7111246Z     
2025-03-14T04:33:23.7111350Z     Example:
2025-03-14T04:33:23.7111471Z         >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7111582Z         >>> # both workers
2025-03-14T04:33:23.7111680Z         >>> def add(x, y):
2025-03-14T04:33:23.7111843Z         >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2025-03-14T04:33:23.7111955Z         >>>     return x + y, (x + y).to(2)
2025-03-14T04:33:23.7112059Z         >>>
2025-03-14T04:33:23.7112155Z         >>> # on worker 0
2025-03-14T04:33:23.7112306Z         >>> options = TensorPipeRpcBackendOptions(
2025-03-14T04:33:23.7112416Z         >>>     num_worker_threads=8,
2025-03-14T04:33:23.7112551Z         >>>     device_maps={"worker1": {0: 1}}
2025-03-14T04:33:23.7112713Z         >>> # maps worker0's cuda:0 to worker1's cuda:1
2025-03-14T04:33:23.7112817Z         >>> )
2025-03-14T04:33:23.7112950Z         >>> options.set_device_map("worker1", {1: 2})
2025-03-14T04:33:23.7113098Z         >>> # maps worker0's cuda:1 to worker1's cuda:2
2025-03-14T04:33:23.7113190Z         >>>
2025-03-14T04:33:23.7113305Z         >>> rpc.init_rpc(
2025-03-14T04:33:23.7113401Z         >>>     "worker0",
2025-03-14T04:33:23.7113505Z         >>>     rank=0,
2025-03-14T04:33:23.7113604Z         >>>     world_size=2,
2025-03-14T04:33:23.7113756Z         >>>     backend=rpc.BackendType.TENSORPIPE,
2025-03-14T04:33:23.7113874Z         >>>     rpc_backend_options=options
2025-03-14T04:33:23.7113978Z         >>> )
2025-03-14T04:33:23.7114150Z         >>>
2025-03-14T04:33:23.7114250Z         >>> x = torch.ones(2)
2025-03-14T04:33:23.7114431Z         >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2025-03-14T04:33:23.7114620Z         >>> # The first argument will be moved to cuda:1 on worker1. When
2025-03-14T04:33:23.7114825Z         >>> # sending the return value back, it will follow the invert of
2025-03-14T04:33:23.7115003Z         >>> # the device map, and hence will be moved back to cuda:0 and
2025-03-14T04:33:23.7115120Z         >>> # cuda:1 on worker0
2025-03-14T04:33:23.7115273Z         >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2025-03-14T04:33:23.7115436Z         >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2025-03-14T04:33:23.7115520Z     
2025-03-14T04:33:23.7115819Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7115932Z     
2025-03-14T04:33:23.7116049Z       warnings.warn(msg)
2025-03-14T04:33:23.7116137Z     
2025-03-14T04:33:23.7116354Z --- Parse Warning: 77 / 116 ---
2025-03-14T04:33:23.7117445Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19.
2025-03-14T04:33:23.7117723Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7117807Z     
2025-03-14T04:33:23.7118027Z     It has the same API as ``torch.autograd.profiler.profile`` class,
2025-03-14T04:33:23.7118306Z     except that it enables profiling on all threads running RPC server request callbacks.
2025-03-14T04:33:23.7118408Z     
2025-03-14T04:33:23.7118686Z     Context manager that manages autograd profiler state and holds a summary of results.
2025-03-14T04:33:23.7118935Z     Under the hood it just records events of functions being executed in C++ and
2025-03-14T04:33:23.7119356Z     exposes those events to Python. You can wrap any code into it and it will
2025-03-14T04:33:23.7119506Z     only report runtime of PyTorch functions.
2025-03-14T04:33:23.7119773Z     Note: profiler is thread local and is automatically propagated into the async tasks
2025-03-14T04:33:23.7119875Z     
2025-03-14T04:33:23.7119971Z     Args:
2025-03-14T04:33:23.7120254Z         enabled (bool, optional): Setting this to False makes this context manager a no-op.
2025-03-14T04:33:23.7120356Z             Default: ``True``.
2025-03-14T04:33:23.7120456Z     
2025-03-14T04:33:23.7120742Z         use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
2025-03-14T04:33:23.7120959Z             Adds approximately 4us of overhead to each tensor operation.
2025-03-14T04:33:23.7121060Z             Default: ``False``
2025-03-14T04:33:23.7121163Z     
2025-03-14T04:33:23.7121395Z         record_shapes (bool, optional): If shapes recording is set, information
2025-03-14T04:33:23.7121647Z             about input dimensions will be collected. This allows one to see which
2025-03-14T04:33:23.7121859Z             dimensions have been used under the hood and further group by them
2025-03-14T04:33:23.7122092Z             using prof.key_averages(group_by_input_shape=True). Please note that
2025-03-14T04:33:23.7122349Z             shape recording might skew your profiling data. It is recommended to
2025-03-14T04:33:23.7122602Z             use separate runs with and without shape recording to validate the timing.
2025-03-14T04:33:23.7122834Z             Most likely the skew will be negligible for bottom most events (in a case
2025-03-14T04:33:23.7123060Z             of nested function calls). But for higher level functions the total
2025-03-14T04:33:23.7123265Z             self cpu time might be artificially increased because of the shape
2025-03-14T04:33:23.7123377Z             collection.
2025-03-14T04:33:23.7123461Z     
2025-03-14T04:33:23.7123743Z         profile_memory (bool, optional): Whether to report memory usage, default: ``False``
2025-03-14T04:33:23.7123831Z     
2025-03-14T04:33:23.7123940Z     .. warning:
2025-03-14T04:33:23.7124145Z         Enabling memory profiling incurs additional profiler overhead
2025-03-14T04:33:23.7124242Z     
2025-03-14T04:33:23.7124334Z     .. warning:
2025-03-14T04:33:23.7124599Z         Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_),
2025-03-14T04:33:23.7124800Z         one cannot use the profiler with ``use_cuda = True`` to benchmark
2025-03-14T04:33:23.7125052Z         DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
2025-03-14T04:33:23.7125221Z         please use ``use_cuda = False`` or ``num_workers = 0``.
2025-03-14T04:33:23.7125322Z     
2025-03-14T04:33:23.7125413Z     Example:
2025-03-14T04:33:23.7125555Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7125694Z         >>> # On worker 0:
2025-03-14T04:33:23.7125804Z         >>> import torch
2025-03-14T04:33:23.7125941Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7126099Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-14T04:33:23.7126226Z         >>> x, y = torch.tensor(1), torch.tensor(2)
2025-03-14T04:33:23.7126363Z         >>> outer_profile_rref = rpc.remote(
2025-03-14T04:33:23.7126536Z         ...     dst_worker_name, rpc._server_process_global_profile
2025-03-14T04:33:23.7126637Z         ... )
2025-03-14T04:33:23.7126774Z         >>> outer_profile_rref.rpc_sync().__enter__()
2025-03-14T04:33:23.7126927Z         >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y))
2025-03-14T04:33:23.7127064Z         >>> inner_profile_rref = rpc.remote(
2025-03-14T04:33:23.7127229Z         ...     dst_worker_name, rpc._server_process_global_profile
2025-03-14T04:33:23.7127329Z         ... )
2025-03-14T04:33:23.7127466Z         >>> inner_profile_rref.rpc_sync().__enter__()
2025-03-14T04:33:23.7127632Z         >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y))
2025-03-14T04:33:23.7127809Z         >>> inner_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-14T04:33:23.7128488Z         >>> outer_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-14T04:33:23.7128651Z         >>> print(inner_profile_rref.rpc_sync().key_averages())
2025-03-14T04:33:23.7128912Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:23.7129225Z         Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-14T04:33:23.7129471Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:23.7129681Z         sub        85.06%           76.275us         100.00%          89.667us         89.667us         1
2025-03-14T04:33:23.7129883Z         empty      14.94%           13.392us         14.94%           13.392us         13.392us         1
2025-03-14T04:33:23.7130124Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:23.7130241Z         Self CPU time total: 89.667us
2025-03-14T04:33:23.7130423Z         >>> print(outer_profile_rref.rpc_sync().key_averages())
2025-03-14T04:33:23.7130675Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:23.7130987Z         Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-14T04:33:23.7131229Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:23.7131414Z         sub        35.65%           76.275us         41.91%           89.667us         89.667us         1
2025-03-14T04:33:23.7131617Z         empty      12.67%           27.101us         12.67%           27.101us         13.551us         2
2025-03-14T04:33:23.7131805Z         add        51.68%           110.550us        58.09%           124.259us        124.259us        1
2025-03-14T04:33:23.7132046Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-14T04:33:23.7132160Z         Self CPU time total: 213.926us
2025-03-14T04:33:23.7132278Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7132364Z     
2025-03-14T04:33:23.7132475Z         >>> # On worker 1:
2025-03-14T04:33:23.7132606Z         >>> import torch.distributed.rpc as rpc
2025-03-14T04:33:23.7132757Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-14T04:33:23.7132923Z         >>> # wait for worker 0 to finish work, and then shutdown.
2025-03-14T04:33:23.7133033Z         >>> rpc.shutdown()
2025-03-14T04:33:23.7133117Z     
2025-03-14T04:33:23.7133405Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7133513Z     
2025-03-14T04:33:23.7133623Z       warnings.warn(msg)
2025-03-14T04:33:23.7133709Z     
2025-03-14T04:33:23.7133932Z --- Parse Warning: 78 / 116 ---
2025-03-14T04:33:23.7134912Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=34.
2025-03-14T04:33:23.7135184Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7135270Z     
2025-03-14T04:33:23.7135544Z     :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2025-03-14T04:33:23.7135822Z     to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2025-03-14T04:33:23.7136095Z     the local components of :class:`DTensor`, call the function, and wrap the outputs to
2025-03-14T04:33:23.7136262Z     :class:`DTensor` according to the ``out_placements``.
2025-03-14T04:33:23.7136358Z     
2025-03-14T04:33:23.7136446Z     Args:
2025-03-14T04:33:23.7136692Z         func (Callable): the function to be applied on each local shard of
2025-03-14T04:33:23.7136793Z             :class:`DTensor` s.
2025-03-14T04:33:23.7137031Z         out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2025-03-14T04:33:23.7137291Z             the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2025-03-14T04:33:23.7137541Z             If the flattened ``output`` is a single value, the ``out_placements`` should be
2025-03-14T04:33:23.7137783Z             of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2025-03-14T04:33:23.7138038Z             values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2025-03-14T04:33:23.7138163Z             mapping to the flattened ``output``.
2025-03-14T04:33:23.7138378Z             Besides, for :class:`Tensor` output, we use `PlacementType` as its
2025-03-14T04:33:23.7138651Z             placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2025-03-14T04:33:23.7138762Z             should be `None`.
2025-03-14T04:33:23.7139001Z             Note that the only exception is when no :class:`DTensor` argument is passed
2025-03-14T04:33:23.7139229Z             in. In this case, even if `out_placements` is not `None`, the result function
2025-03-14T04:33:23.7139510Z             should ignore the desired placements because the function is not running with
2025-03-14T04:33:23.7139624Z             :class:`DTensor` s.
2025-03-14T04:33:23.7139793Z         in_placements (Tuple[`PlacementType`, ...], optional):
2025-03-14T04:33:23.7140078Z             the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2025-03-14T04:33:23.7140311Z             If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2025-03-14T04:33:23.7140548Z             placements of each :class:`DTensor` argument is the same as the required
2025-03-14T04:33:23.7140730Z             placements or not. If the placements are not the same and
2025-03-14T04:33:23.7140985Z             ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2025-03-14T04:33:23.7141225Z             ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2025-03-14T04:33:23.7141492Z             the required sharding placements before passing its local tensor to ``func``.
2025-03-14T04:33:23.7141716Z             The only exception is when required placements are not ``None`` and the
2025-03-14T04:33:23.7141960Z             argument is a :class:`torch.Tensor`. In this case, the placements examination
2025-03-14T04:33:23.7142177Z             will be skipped and the argument will be directly passed to ``func``.
2025-03-14T04:33:23.7142408Z             If ``in_placements`` is ``None``, no placements examination will be performed.
2025-03-14T04:33:23.7142554Z             Default: None
2025-03-14T04:33:23.7142703Z         device_mesh (:class:`DeviceMesh`, optional):
2025-03-14T04:33:23.7142921Z             the device mesh that all the :class:`DTensor` s are placed on. If not
2025-03-14T04:33:23.7143160Z             specified, this will be inferred from the input :class:`DTensor` s' device
2025-03-14T04:33:23.7143397Z             mesh. `local_map` requires every :class:`DTensor` s to be placed on the same
2025-03-14T04:33:23.7143521Z             device mesh. Default: None.
2025-03-14T04:33:23.7143648Z         redistribute_inputs (bool, optional):
2025-03-14T04:33:23.7143911Z             the bool value indicating whether to reshard the input :class:`DTensor` s when
2025-03-14T04:33:23.7144156Z             their placements are different from the required input placements. If this
2025-03-14T04:33:23.7144388Z             value is ``False`` and some :class:`DTensor` input has a different placement,
2025-03-14T04:33:23.7144531Z             an exception will be raised. Default: False.
2025-03-14T04:33:23.7144623Z     
2025-03-14T04:33:23.7144710Z     Returns:
2025-03-14T04:33:23.7144974Z         A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2025-03-14T04:33:23.7145240Z         and returns a :class:`DTensor` constructed from the return value of ``func``.
2025-03-14T04:33:23.7145330Z     
2025-03-14T04:33:23.7145416Z     Raises:
2025-03-14T04:33:23.7145672Z         AssertionError: If the input :class:`DTensor` is not placed on the same device
2025-03-14T04:33:23.7145909Z             mesh, or if they are placed on a different device mesh than the ``device_mesh``
2025-03-14T04:33:23.7146023Z             argument passed in.
2025-03-14T04:33:23.7146111Z     
2025-03-14T04:33:23.7146362Z         AssertionError: For any non-DTensor output, we require its corresponding
2025-03-14T04:33:23.7146616Z             output placement in ``out_placements`` be None. An AssertionError will be raised
2025-03-14T04:33:23.7146733Z             if this is not the case.
2025-03-14T04:33:23.7146816Z     
2025-03-14T04:33:23.7147088Z         ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2025-03-14T04:33:23.7147244Z             a redistribution according to ``in_placements``.
2025-03-14T04:33:23.7147337Z     
2025-03-14T04:33:23.7147423Z     Example:
2025-03-14T04:33:23.7147550Z         >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7147715Z         >>> def mm_allreduce_forward(device_mesh, W, X):
2025-03-14T04:33:23.7147848Z         >>>     partial_sum_tensor = torch.mm(W, X)
2025-03-14T04:33:23.7148088Z         >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2025-03-14T04:33:23.7148203Z         >>>     return reduced_tensor
2025-03-14T04:33:23.7148288Z         >>>
2025-03-14T04:33:23.7148419Z         >>> W = torch.randn(12, 8, requires_grad=False)
2025-03-14T04:33:23.7148555Z         >>> X = torch.randn(8, 16, requires_grad=False)
2025-03-14T04:33:23.7148658Z         >>> Y = torch.mm(W, X)
2025-03-14T04:33:23.7149044Z         >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2025-03-14T04:33:23.7149236Z         >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2025-03-14T04:33:23.7149339Z         >>>
2025-03-14T04:33:23.7149615Z         >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
2025-03-14T04:33:23.7149752Z         >>> local_mm_allreduce_forward = local_map(
2025-03-14T04:33:23.7149866Z         >>>     mm_allreduce_forward,
2025-03-14T04:33:23.7149997Z         >>>     out_placements=[Replicate()],
2025-03-14T04:33:23.7150123Z         >>>     in_placements=[col_wise, row_wise],
2025-03-14T04:33:23.7150247Z         >>>     device_mesh=device_mesh,
2025-03-14T04:33:23.7150339Z         >>> )
2025-03-14T04:33:23.7150433Z         >>>
2025-03-14T04:33:23.7150541Z         >>> W_dt = distribute_tensor(
2025-03-14T04:33:23.7150659Z         ...     W, device_mesh, (col_wise)
2025-03-14T04:33:23.7150885Z         ... )  # col-wisely sharded W tensor
2025-03-14T04:33:23.7151004Z         >>> X_dt = distribute_tensor(
2025-03-14T04:33:23.7151117Z         ...     X, device_mesh, (row_wise)
2025-03-14T04:33:23.7151243Z         ... )  # row-wisely sharded X tensor
2025-03-14T04:33:23.7151361Z         >>> Y_dt = local_mm_allreduce_forward(
2025-03-14T04:33:23.7151474Z         ...     device_mesh, W_dt, X_dt
2025-03-14T04:33:23.7151625Z         ... )  # apply local_mm_allreduce_forward to DTensors
2025-03-14T04:33:23.7151716Z     
2025-03-14T04:33:23.7151923Z     .. note:: This API is currently experimental and subject to change
2025-03-14T04:33:23.7152016Z     
2025-03-14T04:33:23.7152271Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7152361Z     
2025-03-14T04:33:23.7152462Z       warnings.warn(msg)
2025-03-14T04:33:23.7152553Z     
2025-03-14T04:33:23.7152764Z --- Parse Warning: 79 / 116 ---
2025-03-14T04:33:23.7153849Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=26.
2025-03-14T04:33:23.7154152Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7154250Z     
2025-03-14T04:33:23.7154531Z     :meth:`register_sharding` is an experimental API that allows users to register sharding
2025-03-14T04:33:23.7154778Z     strategies for an operator when the tensor inputs and outputs are DTensor.
2025-03-14T04:33:23.7155030Z     It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2025-03-14T04:33:23.7155280Z     e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2025-03-14T04:33:23.7155552Z     when users would like to overwrite default sharding strategies of existing operators.
2025-03-14T04:33:23.7155649Z     
2025-03-14T04:33:23.7155741Z     Args:
2025-03-14T04:33:23.7155882Z         op (Union[OpOverload, List[OpOverload]]):
2025-03-14T04:33:23.7156077Z             An op or a list of ops to register the customized sharding function.
2025-03-14T04:33:23.7156170Z     
2025-03-14T04:33:23.7156261Z     Returns:
2025-03-14T04:33:23.7156536Z         A function decorator which can be used to wrap a function that defines the sharding
2025-03-14T04:33:23.7156839Z         strategy for the operator specified in ``op``. The defined sharding strategy will be
2025-03-14T04:33:23.7157122Z         registered to DTensor and will override the default sharding strategy if DTensor has
2025-03-14T04:33:23.7157422Z         already implemented the operator. The customized sharding function takes the same inputs
2025-03-14T04:33:23.7157671Z         as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2025-03-14T04:33:23.7157944Z         replaced by a tensor-like object that DTensor uses internally). The function should
2025-03-14T04:33:23.7158225Z         return a sequence of 2-tuples, each specifying acceptable output placements and its
2025-03-14T04:33:23.7158348Z         corresponding intput placements.
2025-03-14T04:33:23.7158439Z     
2025-03-14T04:33:23.7158527Z     Example:
2025-03-14T04:33:23.7158655Z         >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7158788Z         >>> @register_sharding(aten._softmax.default)
2025-03-14T04:33:23.7158959Z         >>> def custom_softmax_sharding(x, dim, half_to_float):
2025-03-14T04:33:23.7159102Z         >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2025-03-14T04:33:23.7159323Z         >>>     acceptable_shardings = []
2025-03-14T04:33:23.7159410Z         >>>
2025-03-14T04:33:23.7159600Z         >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2025-03-14T04:33:23.7159747Z         >>>     acceptable_shardings.append(all_replicate)
2025-03-14T04:33:23.7159844Z         >>>
2025-03-14T04:33:23.7160011Z         >>>     for sharding_dim in range(x.ndim):
2025-03-14T04:33:23.7160158Z         >>>         if sharding_dim != softmax_dim:
2025-03-14T04:33:23.7160267Z         >>>             all_sharded = (
2025-03-14T04:33:23.7160383Z         >>>                 [Shard(sharding_dim)],
2025-03-14T04:33:23.7160518Z         >>>                 [Shard(sharding_dim), None, None],
2025-03-14T04:33:23.7160608Z         >>>             )
2025-03-14T04:33:23.7160759Z         >>>             acceptable_shardings.append(all_sharded)
2025-03-14T04:33:23.7160846Z         >>>
2025-03-14T04:33:23.7160968Z         >>>     return acceptable_shardings
2025-03-14T04:33:23.7161052Z     
2025-03-14T04:33:23.7161257Z     .. note:: This API is currently experimental and subject to change
2025-03-14T04:33:23.7161342Z     
2025-03-14T04:33:23.7161600Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7161684Z     
2025-03-14T04:33:23.7161789Z       warnings.warn(msg)
2025-03-14T04:33:23.7161873Z     
2025-03-14T04:33:23.7162098Z --- Parse Warning: 80 / 116 ---
2025-03-14T04:33:23.7163106Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=403.
2025-03-14T04:33:23.7163415Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7163501Z     
2025-03-14T04:33:23.7163903Z     Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2025-03-14T04:33:23.7164229Z     ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2025-03-14T04:33:23.7164329Z     
2025-03-14T04:33:23.7164425Z     Keyword Args:
2025-03-14T04:33:23.7164641Z         input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-14T04:33:23.7164973Z             The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-03-14T04:33:23.7165348Z             DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-03-14T04:33:23.7165478Z             as a placeholder. default: None.
2025-03-14T04:33:23.7165731Z         desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-14T04:33:23.7166136Z             The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-14T04:33:23.7166547Z             have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-03-14T04:33:23.7166687Z         input_kwarg_layouts (Dict[str, Placement]):
2025-03-14T04:33:23.7167076Z             The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-03-14T04:33:23.7167178Z             default: None
2025-03-14T04:33:23.7167366Z         desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-03-14T04:33:23.7167736Z             The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-14T04:33:23.7167906Z             have the desired DTensor layouts. default: None.
2025-03-14T04:33:23.7168028Z         use_local_output (bool, optional):
2025-03-14T04:33:23.7168408Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-03-14T04:33:23.7168501Z     Returns:
2025-03-14T04:33:23.7168834Z         A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2025-03-14T04:33:23.7168923Z     
2025-03-14T04:33:23.7169036Z     Example::
2025-03-14T04:33:23.7169151Z         >>> # xdoctest: +SKIP(failing)
2025-03-14T04:33:23.7169482Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2025-03-14T04:33:23.7169708Z         >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-14T04:33:23.7169840Z         >>> ...
2025-03-14T04:33:23.7170150Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-14T04:33:23.7170324Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-14T04:33:23.7170415Z         >>>
2025-03-14T04:33:23.7170772Z         >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2025-03-14T04:33:23.7170928Z         >>> # and then redistributed to Replicated DTensor.
2025-03-14T04:33:23.7171052Z         >>> parallelize_module(
2025-03-14T04:33:23.7171192Z         >>>     block, # this can be a submodule or module
2025-03-14T04:33:23.7171306Z         >>>     tp_mesh,
2025-03-14T04:33:23.7171417Z         >>>     parallelize_plan={
2025-03-14T04:33:23.7171559Z         >>>         "attn": PrepareModuleInput(
2025-03-14T04:33:23.7171707Z         >>>             input_layouts=(Shard(0), None, None, ...),
2025-03-14T04:33:23.7171898Z         >>>             desired_input_layouts=(Replicate(), None, None, ...)
2025-03-14T04:33:23.7171990Z         >>>         ),
2025-03-14T04:33:23.7172121Z         >>>     }
2025-03-14T04:33:23.7172214Z         >>> )
2025-03-14T04:33:23.7172313Z     
2025-03-14T04:33:23.7172566Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7172669Z     
2025-03-14T04:33:23.7172771Z       warnings.warn(msg)
2025-03-14T04:33:23.7172871Z     
2025-03-14T04:33:23.7173066Z --- Parse Warning: 81 / 116 ---
2025-03-14T04:33:23.7174080Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=562.
2025-03-14T04:33:23.7174344Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7174443Z     
2025-03-14T04:33:23.7174836Z     Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2025-03-14T04:33:23.7175180Z     ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2025-03-14T04:33:23.7175265Z     
2025-03-14T04:33:23.7175370Z     Keyword Args:
2025-03-14T04:33:23.7175540Z         output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-14T04:33:23.7175919Z             The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-03-14T04:33:23.7176299Z             DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-03-14T04:33:23.7176460Z             ``None`` need to be specified as a placeholder.
2025-03-14T04:33:23.7176661Z         desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-14T04:33:23.7177590Z             The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-03-14T04:33:23.7177716Z             have the desired DTensor layouts.
2025-03-14T04:33:23.7177853Z         use_local_output (bool, optional):
2025-03-14T04:33:23.7178219Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-03-14T04:33:23.7178322Z     Returns:
2025-03-14T04:33:23.7178619Z         A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2025-03-14T04:33:23.7178721Z     
2025-03-14T04:33:23.7178817Z     Example::
2025-03-14T04:33:23.7178945Z         >>> # xdoctest: +SKIP(failing)
2025-03-14T04:33:23.7179264Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2025-03-14T04:33:23.7179474Z         >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-14T04:33:23.7179562Z         >>> ...
2025-03-14T04:33:23.7179906Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-14T04:33:23.7180084Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-14T04:33:23.7180188Z         >>>
2025-03-14T04:33:23.7180585Z         >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2025-03-14T04:33:23.7180738Z         >>> # and then redistributed to Sharded DTensor.
2025-03-14T04:33:23.7180848Z         >>> parallelize_module(
2025-03-14T04:33:23.7181001Z         >>>     block, # this can be a submodule or module
2025-03-14T04:33:23.7181098Z         >>>     tp_mesh,
2025-03-14T04:33:23.7181254Z         >>>     parallelize_plan = PrepareModuleOutput(
2025-03-14T04:33:23.7181376Z         >>>         output_layouts=Replicate(),
2025-03-14T04:33:23.7181518Z         >>>         desired_output_layouts=Shard(0)
2025-03-14T04:33:23.7181608Z         >>>     )
2025-03-14T04:33:23.7181710Z         >>> )
2025-03-14T04:33:23.7181796Z     
2025-03-14T04:33:23.7182067Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7182154Z     
2025-03-14T04:33:23.7182285Z       warnings.warn(msg)
2025-03-14T04:33:23.7182384Z     
2025-03-14T04:33:23.7182579Z --- Parse Warning: 82 / 116 ---
2025-03-14T04:33:23.7183671Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=55.
2025-03-14T04:33:23.7183948Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7184036Z     
2025-03-14T04:33:23.7184358Z     Creates a multivariate normal distribution with covariance matrix having a low-rank form
2025-03-14T04:33:23.7184552Z     parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
2025-03-14T04:33:23.7184651Z     
2025-03-14T04:33:23.7184838Z         covariance_matrix = cov_factor @ cov_factor.T + cov_diag
2025-03-14T04:33:23.7184928Z     
2025-03-14T04:33:23.7185034Z     Example:
2025-03-14T04:33:23.7185190Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-03-14T04:33:23.7185351Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:23.7185477Z         >>> m = LowRankMultivariateNormal(
2025-03-14T04:33:23.7185676Z         ...     torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2)
2025-03-14T04:33:23.7185792Z         ... )
2025-03-14T04:33:23.7186096Z         >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
2025-03-14T04:33:23.7186202Z         tensor([-0.2102, -0.5429])
2025-03-14T04:33:23.7186301Z     
2025-03-14T04:33:23.7186389Z     Args:
2025-03-14T04:33:23.7186636Z         loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
2025-03-14T04:33:23.7186898Z         cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
2025-03-14T04:33:23.7187037Z             `batch_shape + event_shape + (rank,)`
2025-03-14T04:33:23.7187293Z         cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
2025-03-14T04:33:23.7187423Z             `batch_shape + event_shape`
2025-03-14T04:33:23.7187509Z     
2025-03-14T04:33:23.7187608Z     Note:
2025-03-14T04:33:23.7187879Z         The computation for determinant and inverse of covariance matrix is avoided when
2025-03-14T04:33:23.7188135Z         `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
2025-03-14T04:33:23.7188347Z         <https://en.wikipedia.org/wiki/Woodbury_matrix_identity>`_ and
2025-03-14T04:33:23.7188650Z         `matrix determinant lemma <https://en.wikipedia.org/wiki/Matrix_determinant_lemma>`_.
2025-03-14T04:33:23.7188904Z         Thanks to these formulas, we just need to compute the determinant and inverse of
2025-03-14T04:33:23.7189044Z         the small size "capacitance" matrix::
2025-03-14T04:33:23.7189184Z     
2025-03-14T04:33:23.7189383Z             capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
2025-03-14T04:33:23.7189468Z     
2025-03-14T04:33:23.7189735Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7189819Z     
2025-03-14T04:33:23.7189936Z       warnings.warn(msg)
2025-03-14T04:33:23.7190019Z     
2025-03-14T04:33:23.7190222Z --- Parse Warning: 83 / 116 ---
2025-03-14T04:33:23.7191208Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13.
2025-03-14T04:33:23.7191484Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7191569Z     
2025-03-14T04:33:23.7191808Z     The `MixtureSameFamily` distribution implements a (batch of) mixture
2025-03-14T04:33:23.7192058Z     distribution where all component are from different parameterizations of
2025-03-14T04:33:23.7192288Z     the same distribution type. It is parameterized by a `Categorical`
2025-03-14T04:33:23.7192513Z     "selecting distribution" (over `k` component) and a component
2025-03-14T04:33:23.7192733Z     distribution, i.e., a `Distribution` with a rightmost batch shape
2025-03-14T04:33:23.7192901Z     (equal to `[k]`) which indexes each (batch of) component.
2025-03-14T04:33:23.7192999Z     
2025-03-14T04:33:23.7193096Z     Examples::
2025-03-14T04:33:23.7193195Z     
2025-03-14T04:33:23.7193320Z         >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.7193539Z         >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2025-03-14T04:33:23.7193659Z         >>> # weighted normal distributions
2025-03-14T04:33:23.7193799Z         >>> mix = D.Categorical(torch.ones(5,))
2025-03-14T04:33:23.7193951Z         >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2025-03-14T04:33:23.7194095Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-03-14T04:33:23.7194182Z     
2025-03-14T04:33:23.7194400Z         >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2025-03-14T04:33:23.7194538Z         >>> # weighted bivariate normal distributions
2025-03-14T04:33:23.7194677Z         >>> mix = D.Categorical(torch.ones(5,))
2025-03-14T04:33:23.7194799Z         >>> comp = D.Independent(D.Normal(
2025-03-14T04:33:23.7194951Z         ...          torch.randn(5,2), torch.rand(5,2)), 1)
2025-03-14T04:33:23.7195117Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-03-14T04:33:23.7195219Z     
2025-03-14T04:33:23.7195406Z         >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2025-03-14T04:33:23.7195627Z         >>> # consisting of 5 random weighted bivariate normal distributions
2025-03-14T04:33:23.7195752Z         >>> mix = D.Categorical(torch.rand(3,5))
2025-03-14T04:33:23.7195883Z         >>> comp = D.Independent(D.Normal(
2025-03-14T04:33:23.7196027Z         ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2025-03-14T04:33:23.7196165Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-03-14T04:33:23.7196252Z     
2025-03-14T04:33:23.7196343Z     Args:
2025-03-14T04:33:23.7196569Z         mixture_distribution: `torch.distributions.Categorical`-like
2025-03-14T04:33:23.7196762Z             instance. Manages the probability of selecting component.
2025-03-14T04:33:23.7196949Z             The number of categories must match the rightmost batch
2025-03-14T04:33:23.7197143Z             dimension of the `component_distribution`. Must have either
2025-03-14T04:33:23.7197304Z             scalar `batch_shape` or `batch_shape` matching
2025-03-14T04:33:23.7197445Z             `component_distribution.batch_shape[:-1]`
2025-03-14T04:33:23.7197685Z         component_distribution: `torch.distributions.Distribution`-like
2025-03-14T04:33:23.7197865Z             instance. Right-most batch dimension indexes component.
2025-03-14T04:33:23.7197962Z     
2025-03-14T04:33:23.7198239Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7198364Z     
2025-03-14T04:33:23.7198464Z       warnings.warn(msg)
2025-03-14T04:33:23.7198569Z     
2025-03-14T04:33:23.7198761Z --- Parse Warning: 84 / 116 ---
2025-03-14T04:33:23.7199831Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=111.
2025-03-14T04:33:23.7200096Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7200197Z     
2025-03-14T04:33:23.7200385Z     Creates a RelaxedBernoulli distribution, parametrized by
2025-03-14T04:33:23.7200593Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2025-03-14T04:33:23.7200812Z     (but not both). This is a relaxed version of the `Bernoulli` distribution,
2025-03-14T04:33:23.7201017Z     so the values are in (0, 1), and has reparametrizable samples.
2025-03-14T04:33:23.7201105Z     
2025-03-14T04:33:23.7201216Z     Example::
2025-03-14T04:33:23.7201334Z     
2025-03-14T04:33:23.7201490Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:23.7201626Z         >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2025-03-14T04:33:23.7201773Z         ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2025-03-14T04:33:23.7201870Z         >>> m.sample()
2025-03-14T04:33:23.7202007Z         tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2025-03-14T04:33:23.7202094Z     
2025-03-14T04:33:23.7202195Z     Args:
2025-03-14T04:33:23.7202341Z         temperature (Tensor): relaxation temperature
2025-03-14T04:33:23.7202530Z         probs (Number, Tensor): the probability of sampling `1`
2025-03-14T04:33:23.7202697Z         logits (Number, Tensor): the log-odds of sampling `1`
2025-03-14T04:33:23.7202795Z     
2025-03-14T04:33:23.7203058Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7203161Z     
2025-03-14T04:33:23.7203266Z       warnings.warn(msg)
2025-03-14T04:33:23.7203364Z     
2025-03-14T04:33:23.7203563Z --- Parse Warning: 85 / 116 ---
2025-03-14T04:33:23.7204635Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=101.
2025-03-14T04:33:23.7204900Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7204993Z     
2025-03-14T04:33:23.7205211Z     Creates a RelaxedOneHotCategorical distribution parametrized by
2025-03-14T04:33:23.7205423Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2025-03-14T04:33:23.7205662Z     This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2025-03-14T04:33:23.7205842Z     its samples are on simplex, and are reparametrizable.
2025-03-14T04:33:23.7205929Z     
2025-03-14T04:33:23.7206032Z     Example::
2025-03-14T04:33:23.7206119Z     
2025-03-14T04:33:23.7206274Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:23.7206434Z         >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2025-03-14T04:33:23.7206577Z         ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2025-03-14T04:33:23.7206671Z         >>> m.sample()
2025-03-14T04:33:23.7206803Z         tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2025-03-14T04:33:23.7206887Z     
2025-03-14T04:33:23.7206975Z     Args:
2025-03-14T04:33:23.7207132Z         temperature (Tensor): relaxation temperature
2025-03-14T04:33:23.7207252Z         probs (Tensor): event probabilities
2025-03-14T04:33:23.7207452Z         logits (Tensor): unnormalized log probability for each event
2025-03-14T04:33:23.7207535Z     
2025-03-14T04:33:23.7207798Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7207937Z     
2025-03-14T04:33:23.7208051Z       warnings.warn(msg)
2025-03-14T04:33:23.7208134Z     
2025-03-14T04:33:23.7208332Z --- Parse Warning: 86 / 116 ---
2025-03-14T04:33:23.7209335Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245.
2025-03-14T04:33:23.7209611Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7209806Z     Return a new dict with new, potentially nested, key value pair
2025-03-14T04:33:23.7209929Z     
2025-03-14T04:33:23.7210029Z         >>> purchase = {
2025-03-14T04:33:23.7210141Z         ...     "name": "Alice",
2025-03-14T04:33:23.7210321Z         ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-14T04:33:23.7210454Z         ...     "credit card": "5555-1234-1234-1234",
2025-03-14T04:33:23.7210545Z         ... }
2025-03-14T04:33:23.7210768Z         >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2025-03-14T04:33:23.7210908Z         {'credit card': '5555-1234-1234-1234',
2025-03-14T04:33:23.7211015Z          'name': 'Alice',
2025-03-14T04:33:23.7211186Z          'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2025-03-14T04:33:23.7211283Z         
2025-03-14T04:33:23.7211537Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7211635Z     
2025-03-14T04:33:23.7211735Z       warnings.warn(msg)
2025-03-14T04:33:23.7211830Z     
2025-03-14T04:33:23.7212017Z --- Parse Warning: 87 / 116 ---
2025-03-14T04:33:23.7213030Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261.
2025-03-14T04:33:23.7213298Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7213462Z     Update value in a (potentially) nested dictionary
2025-03-14T04:33:23.7213551Z     
2025-03-14T04:33:23.7213649Z         inputs:
2025-03-14T04:33:23.7213772Z         d - dictionary on which to operate
2025-03-14T04:33:23.7214003Z         keys - list or tuple giving the location of the value to be changed in d
2025-03-14T04:33:23.7214159Z         func - function to operate on that value
2025-03-14T04:33:23.7214258Z     
2025-03-14T04:33:23.7214451Z         If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2025-03-14T04:33:23.7214702Z         original dictionary with v replaced by func(v), but does not mutate the
2025-03-14T04:33:23.7214808Z         original dictionary.
2025-03-14T04:33:23.7214904Z     
2025-03-14T04:33:23.7215115Z         If k0 is not a key in d, update_in creates nested dictionaries to the depth
2025-03-14T04:33:23.7215346Z         specified by the keys, with the innermost value set to func(default).
2025-03-14T04:33:23.7215433Z     
2025-03-14T04:33:23.7215551Z         >>> inc = lambda x: x + 1
2025-03-14T04:33:23.7215666Z         >>> update_in({"a": 0}, ["a"], inc)
2025-03-14T04:33:23.7215766Z         {'a': 1}
2025-03-14T04:33:23.7215853Z     
2025-03-14T04:33:23.7215952Z         >>> transaction = {
2025-03-14T04:33:23.7216064Z         ...     "name": "Alice",
2025-03-14T04:33:23.7216259Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-14T04:33:23.7216393Z         ...     "credit card": "5555-1234-1234-1234",
2025-03-14T04:33:23.7216478Z         ... }
2025-03-14T04:33:23.7216706Z         >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2025-03-14T04:33:23.7216817Z         {'credit card': '5555-1234-1234-1234',
2025-03-14T04:33:23.7216925Z          'name': 'Alice',
2025-03-14T04:33:23.7217091Z          'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2025-03-14T04:33:23.7217241Z     
2025-03-14T04:33:23.7217364Z         >>> # updating a value when k0 is not in d
2025-03-14T04:33:23.7217512Z         >>> update_in({}, [1, 2, 3], str, default="bar")
2025-03-14T04:33:23.7217609Z         {1: {2: {3: 'bar'}}}
2025-03-14T04:33:23.7217739Z         >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2025-03-14T04:33:23.7217835Z         {1: 'foo', 2: {3: {4: 1}}}
2025-03-14T04:33:23.7217933Z         
2025-03-14T04:33:23.7218185Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7218285Z     
2025-03-14T04:33:23.7218385Z       warnings.warn(msg)
2025-03-14T04:33:23.7218484Z     
2025-03-14T04:33:23.7218670Z --- Parse Warning: 88 / 116 ---
2025-03-14T04:33:23.7219668Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320.
2025-03-14T04:33:23.7219935Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7220117Z     Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2025-03-14T04:33:23.7220224Z     
2025-03-14T04:33:23.7220422Z         If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2025-03-14T04:33:23.7220620Z         ``no_default`` is specified, then it raises KeyError or IndexError.
2025-03-14T04:33:23.7220719Z     
2025-03-14T04:33:23.7220929Z         ``get_in`` is a generalization of ``operator.getitem`` for nested data
2025-03-14T04:33:23.7221078Z         structures such as dictionaries and lists.
2025-03-14T04:33:23.7221160Z     
2025-03-14T04:33:23.7221274Z         >>> transaction = {
2025-03-14T04:33:23.7221373Z         ...     "name": "Alice",
2025-03-14T04:33:23.7221578Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-14T04:33:23.7221698Z         ...     "credit card": "5555-1234-1234-1234",
2025-03-14T04:33:23.7221799Z         ... }
2025-03-14T04:33:23.7221938Z         >>> get_in(["purchase", "items", 0], transaction)
2025-03-14T04:33:23.7222040Z         'Apple'
2025-03-14T04:33:23.7222153Z         >>> get_in(["name"], transaction)
2025-03-14T04:33:23.7222255Z         'Alice'
2025-03-14T04:33:23.7222389Z         >>> get_in(["purchase", "total"], transaction)
2025-03-14T04:33:23.7222554Z         >>> get_in(["purchase", "items", "apple"], transaction)
2025-03-14T04:33:23.7222717Z         >>> get_in(["purchase", "items", 10], transaction)
2025-03-14T04:33:23.7222868Z         >>> get_in(["purchase", "total"], transaction, 0)
2025-03-14T04:33:23.7222955Z         0
2025-03-14T04:33:23.7223083Z         >>> get_in(["y"], {}, no_default=True)
2025-03-14T04:33:23.7223202Z         Traceback (most recent call last):
2025-03-14T04:33:23.7223288Z             ...
2025-03-14T04:33:23.7223397Z         KeyError: 'y'
2025-03-14T04:33:23.7223480Z     
2025-03-14T04:33:23.7223582Z         See Also:
2025-03-14T04:33:23.7223686Z             itertoolz.get
2025-03-14T04:33:23.7223803Z             operator.getitem
2025-03-14T04:33:23.7223889Z         
2025-03-14T04:33:23.7224163Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7224247Z     
2025-03-14T04:33:23.7224362Z       warnings.warn(msg)
2025-03-14T04:33:23.7224445Z     
2025-03-14T04:33:23.7224643Z --- Parse Warning: 89 / 116 ---
2025-03-14T04:33:23.7225644Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373.
2025-03-14T04:33:23.7225916Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7226031Z     Group a collection by a key function
2025-03-14T04:33:23.7226126Z     
2025-03-14T04:33:23.7226320Z         >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2025-03-14T04:33:23.7226480Z         >>> groupby(len, names)  # doctest: +SKIP
2025-03-14T04:33:23.7226643Z         {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2025-03-14T04:33:23.7226743Z     
2025-03-14T04:33:23.7226851Z         >>> iseven = lambda x: x % 2 == 0
2025-03-14T04:33:23.7227038Z         >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2025-03-14T04:33:23.7227153Z         {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2025-03-14T04:33:23.7227251Z     
2025-03-14T04:33:23.7227397Z         Non-callable keys imply grouping on a member.
2025-03-14T04:33:23.7227494Z     
2025-03-14T04:33:23.7227586Z         >>> groupby(
2025-03-14T04:33:23.7227691Z         ...     "gender",
2025-03-14T04:33:23.7227777Z         ...     [
2025-03-14T04:33:23.7227917Z         ...         {"name": "Alice", "gender": "F"},
2025-03-14T04:33:23.7228038Z         ...         {"name": "Bob", "gender": "M"},
2025-03-14T04:33:23.7228179Z         ...         {"name": "Charlie", "gender": "M"},
2025-03-14T04:33:23.7228270Z         ...     ],
2025-03-14T04:33:23.7228389Z         ... )  # doctest:+SKIP
2025-03-14T04:33:23.7228548Z         {'F': [{'gender': 'F', 'name': 'Alice'}],
2025-03-14T04:33:23.7228672Z          'M': [{'gender': 'M', 'name': 'Bob'},
2025-03-14T04:33:23.7228792Z                {'gender': 'M', 'name': 'Charlie'}]}
2025-03-14T04:33:23.7228878Z     
2025-03-14T04:33:23.7229037Z         Not to be confused with ``itertools.groupby``
2025-03-14T04:33:23.7229129Z     
2025-03-14T04:33:23.7229235Z         See Also:
2025-03-14T04:33:23.7229324Z             countby
2025-03-14T04:33:23.7229429Z         
2025-03-14T04:33:23.7229685Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7229786Z     
2025-03-14T04:33:23.7229889Z       warnings.warn(msg)
2025-03-14T04:33:23.7229989Z     
2025-03-14T04:33:23.7230177Z --- Parse Warning: 90 / 116 ---
2025-03-14T04:33:23.7231088Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601.
2025-03-14T04:33:23.7231355Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7231552Z     Applies Batch Normalization over a N-Dimensional input.
2025-03-14T04:33:23.7231636Z     
2025-03-14T04:33:23.7232021Z         The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2025-03-14T04:33:23.7232251Z         `Batch Normalization: Accelerating Deep Network Training by Reducing
2025-03-14T04:33:23.7232481Z         Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2025-03-14T04:33:23.7232567Z     
2025-03-14T04:33:23.7232675Z         .. math::
2025-03-14T04:33:23.7232760Z     
2025-03-14T04:33:23.7232991Z             y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2025-03-14T04:33:23.7233081Z     
2025-03-14T04:33:23.7233325Z         The mean and standard-deviation are calculated per-dimension over all
2025-03-14T04:33:23.7233564Z         mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2025-03-14T04:33:23.7233820Z         are learnable parameter vectors of size `C` (where `C` is the input size).
2025-03-14T04:33:23.7233998Z         By default, the elements of :math:`\gamma` are sampled from
2025-03-14T04:33:23.7234216Z         :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2025-03-14T04:33:23.7234476Z         The standard-deviation is calculated via the biased estimator, equivalent to
2025-03-14T04:33:23.7234612Z         `torch.var(input, unbiased=False)`.
2025-03-14T04:33:23.7234696Z     
2025-03-14T04:33:23.7234943Z         Also by default, during training this layer keeps running estimates of its
2025-03-14T04:33:23.7235172Z         computed mean and variance, which are then used for normalization during
2025-03-14T04:33:23.7235481Z         evaluation. The running estimates are kept with a default :attr:`momentum`
2025-03-14T04:33:23.7235573Z         of 0.1.
2025-03-14T04:33:23.7235672Z     
2025-03-14T04:33:23.7235899Z         If :attr:`track_running_stats` is set to ``False``, this layer then does not
2025-03-14T04:33:23.7236129Z         keep running estimates, and batch statistics are instead used during
2025-03-14T04:33:23.7236235Z         evaluation time as well.
2025-03-14T04:33:23.7236330Z     
2025-03-14T04:33:23.7236423Z         .. note::
2025-03-14T04:33:23.7236656Z             This :attr:`momentum` argument is different from one used in optimizer
2025-03-14T04:33:23.7236881Z             classes and the conventional notion of momentum. Mathematically, the
2025-03-14T04:33:23.7237029Z             update rule for running statistics here is
2025-03-14T04:33:23.7237299Z             :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2025-03-14T04:33:23.7237517Z             where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2025-03-14T04:33:23.7237622Z             new observed value.
2025-03-14T04:33:23.7237745Z     
2025-03-14T04:33:23.7238046Z         Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2025-03-14T04:33:23.7238311Z         statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2025-03-14T04:33:23.7238496Z         Normalization or Spatio-temporal Batch Normalization.
2025-03-14T04:33:23.7238591Z     
2025-03-14T04:33:23.7238740Z         Currently :class:`SyncBatchNorm` only supports
2025-03-14T04:33:23.7239030Z         :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2025-03-14T04:33:23.7239339Z         :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2025-03-14T04:33:23.7239563Z         :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2025-03-14T04:33:23.7239665Z         Network with DDP.
2025-03-14T04:33:23.7239767Z     
2025-03-14T04:33:23.7239857Z         Args:
2025-03-14T04:33:23.7240042Z             num_features: :math:`C` from an expected input of size
2025-03-14T04:33:23.7240147Z                 :math:`(N, C, +)`
2025-03-14T04:33:23.7240352Z             eps: a value added to the denominator for numerical stability.
2025-03-14T04:33:23.7240456Z                 Default: ``1e-5``
2025-03-14T04:33:23.7240664Z             momentum: the value used for the running_mean and running_var
2025-03-14T04:33:23.7240909Z                 computation. Can be set to ``None`` for cumulative moving average
2025-03-14T04:33:23.7241047Z                 (i.e. simple average). Default: 0.1
2025-03-14T04:33:23.7241258Z             affine: a boolean value that when set to ``True``, this module has
2025-03-14T04:33:23.7241425Z                 learnable affine parameters. Default: ``True``
2025-03-14T04:33:23.7241640Z             track_running_stats: a boolean value that when set to ``True``, this
2025-03-14T04:33:23.7241888Z                 module tracks the running mean and variance, and when set to ``False``,
2025-03-14T04:33:23.7242112Z                 this module does not track such statistics, and initializes statistics
2025-03-14T04:33:23.7242328Z                 buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2025-03-14T04:33:23.7242560Z                 When these buffers are ``None``, this module always uses batch statistics.
2025-03-14T04:33:23.7242727Z                 in both training and eval modes. Default: ``True``
2025-03-14T04:33:23.7242979Z             process_group: synchronization of stats happen within each process group
2025-03-14T04:33:23.7243219Z                 individually. Default behavior is synchronization across the whole
2025-03-14T04:33:23.7243313Z                 world
2025-03-14T04:33:23.7243413Z     
2025-03-14T04:33:23.7243505Z         Shape:
2025-03-14T04:33:23.7243631Z             - Input: :math:`(N, C, +)`
2025-03-14T04:33:23.7243810Z             - Output: :math:`(N, C, +)` (same shape as input)
2025-03-14T04:33:23.7243918Z     
2025-03-14T04:33:23.7244025Z         .. note::
2025-03-14T04:33:23.7244271Z             Synchronization of batchnorm statistics occurs only while training, i.e.
2025-03-14T04:33:23.7244489Z             synchronization is disabled when ``model.eval()`` is set or if
2025-03-14T04:33:23.7244620Z             ``self.training`` is otherwise ``False``.
2025-03-14T04:33:23.7244716Z     
2025-03-14T04:33:23.7244812Z         Examples::
2025-03-14T04:33:23.7244913Z     
2025-03-14T04:33:23.7245016Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7245147Z             >>> # With Learnable Parameters
2025-03-14T04:33:23.7245259Z             >>> m = nn.SyncBatchNorm(100)
2025-03-14T04:33:23.7245400Z             >>> # creating process group (optional)
2025-03-14T04:33:23.7245544Z             >>> # ranks is a list of int identifying rank ids.
2025-03-14T04:33:23.7245662Z             >>> ranks = list(range(8))
2025-03-14T04:33:23.7245772Z             >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-14T04:33:23.7245935Z             >>> # Note: every rank calls into new_group for every
2025-03-14T04:33:23.7246090Z             >>> # process group created, even if that rank is not
2025-03-14T04:33:23.7246232Z             >>> # part of the group.
2025-03-14T04:33:23.7246480Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-14T04:33:23.7246695Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-14T04:33:23.7246818Z             >>> # Without Learnable Parameters
2025-03-14T04:33:23.7247035Z             >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2025-03-14T04:33:23.7247162Z             >>> input = torch.randn(20, 100, 35, 45, 10)
2025-03-14T04:33:23.7247278Z             >>> output = m(input)
2025-03-14T04:33:23.7247365Z     
2025-03-14T04:33:23.7247498Z             >>> # network is nn.BatchNorm layer
2025-03-14T04:33:23.7247778Z             >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2025-03-14T04:33:23.7247955Z             >>> # only single gpu per process is currently supported
2025-03-14T04:33:23.7248178Z             >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2025-03-14T04:33:23.7248306Z             >>>                         sync_bn_network,
2025-03-14T04:33:23.7248437Z             >>>                         device_ids=[args.local_rank],
2025-03-14T04:33:23.7248607Z             >>>                         output_device=args.local_rank)
2025-03-14T04:33:23.7248861Z         
2025-03-14T04:33:23.7249130Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7249216Z     
2025-03-14T04:33:23.7249332Z       warnings.warn(msg)
2025-03-14T04:33:23.7249416Z     
2025-03-14T04:33:23.7249644Z --- Parse Warning: 91 / 116 ---
2025-03-14T04:33:23.7250647Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=825.
2025-03-14T04:33:23.7250925Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7251234Z     Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2025-03-14T04:33:23.7251333Z     
2025-03-14T04:33:23.7251422Z             Args:
2025-03-14T04:33:23.7251676Z                 module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2025-03-14T04:33:23.7251894Z                 process_group (optional): process group to scope synchronization,
2025-03-14T04:33:23.7252020Z                     default is the whole world
2025-03-14T04:33:23.7252106Z     
2025-03-14T04:33:23.7252209Z             Returns:
2025-03-14T04:33:23.7252460Z                 The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2025-03-14T04:33:23.7252797Z                 layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2025-03-14T04:33:23.7253013Z                 a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2025-03-14T04:33:23.7253121Z                 instead.
2025-03-14T04:33:23.7253207Z     
2025-03-14T04:33:23.7253317Z             Example::
2025-03-14T04:33:23.7253403Z     
2025-03-14T04:33:23.7253539Z                 >>> # Network with nn.BatchNorm layer
2025-03-14T04:33:23.7253689Z                 >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:23.7253818Z                 >>> module = torch.nn.Sequential(
2025-03-14T04:33:23.7253944Z                 >>>            torch.nn.Linear(20, 100),
2025-03-14T04:33:23.7254078Z                 >>>            torch.nn.BatchNorm1d(100),
2025-03-14T04:33:23.7254180Z                 >>>          ).cuda()
2025-03-14T04:33:23.7254319Z                 >>> # creating process group (optional)
2025-03-14T04:33:23.7254469Z                 >>> # ranks is a list of int identifying rank ids.
2025-03-14T04:33:23.7254592Z                 >>> ranks = list(range(8))
2025-03-14T04:33:23.7254706Z                 >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-14T04:33:23.7254891Z                 >>> # Note: every rank calls into new_group for every
2025-03-14T04:33:23.7255062Z                 >>> # process group created, even if that rank is not
2025-03-14T04:33:23.7255168Z                 >>> # part of the group.
2025-03-14T04:33:23.7255308Z                 >>> # xdoctest: +SKIP("distributed")
2025-03-14T04:33:23.7255557Z                 >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-14T04:33:23.7255777Z                 >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-14T04:33:23.7256083Z                 >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2025-03-14T04:33:23.7256170Z     
2025-03-14T04:33:23.7256256Z             
2025-03-14T04:33:23.7256523Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7256608Z     
2025-03-14T04:33:23.7256721Z       warnings.warn(msg)
2025-03-14T04:33:23.7256806Z     
2025-03-14T04:33:23.7257018Z --- Parse Warning: 92 / 116 ---
2025-03-14T04:33:23.7257917Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60.
2025-03-14T04:33:23.7258197Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7258286Z     
2025-03-14T04:33:23.7258611Z     Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2025-03-14T04:33:23.7258699Z     
2025-03-14T04:33:23.7258986Z     * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2025-03-14T04:33:23.7259218Z       be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2025-03-14T04:33:23.7259323Z     
2025-03-14T04:33:23.7259638Z     * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2025-03-14T04:33:23.7259908Z       a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2025-03-14T04:33:23.7260082Z       (tuple of `(name, size)` tuples) for `NamedTensor` input.
2025-03-14T04:33:23.7260183Z     
2025-03-14T04:33:23.7260275Z     Shape:
2025-03-14T04:33:23.7260510Z         - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2025-03-14T04:33:23.7260761Z           dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2025-03-14T04:33:23.7260997Z         - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2025-03-14T04:33:23.7261131Z           :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2025-03-14T04:33:23.7261234Z     
2025-03-14T04:33:23.7261327Z     Args:
2025-03-14T04:33:23.7261625Z         dim (Union[int, str]): Dimension to be unflattened
2025-03-14T04:33:23.7261975Z         unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2025-03-14T04:33:23.7262080Z     
2025-03-14T04:33:23.7262175Z     Examples:
2025-03-14T04:33:23.7262304Z         >>> input = torch.randn(2, 50)
2025-03-14T04:33:23.7262410Z         >>> # With tuple of ints
2025-03-14T04:33:23.7262530Z         >>> m = nn.Sequential(
2025-03-14T04:33:23.7262637Z         >>>     nn.Linear(50, 50),
2025-03-14T04:33:23.7262766Z         >>>     nn.Unflatten(1, (2, 5, 5))
2025-03-14T04:33:23.7262856Z         >>> )
2025-03-14T04:33:23.7262975Z         >>> output = m(input)
2025-03-14T04:33:23.7263078Z         >>> output.size()
2025-03-14T04:33:23.7263197Z         torch.Size([2, 2, 5, 5])
2025-03-14T04:33:23.7263296Z         >>> # With torch.Size
2025-03-14T04:33:23.7263413Z         >>> m = nn.Sequential(
2025-03-14T04:33:23.7263518Z         >>>     nn.Linear(50, 50),
2025-03-14T04:33:23.7263667Z         >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2025-03-14T04:33:23.7263755Z         >>> )
2025-03-14T04:33:23.7263903Z         >>> output = m(input)
2025-03-14T04:33:23.7264002Z         >>> output.size()
2025-03-14T04:33:23.7264105Z         torch.Size([2, 2, 5, 5])
2025-03-14T04:33:23.7264242Z         >>> # With namedshape (tuple of tuples)
2025-03-14T04:33:23.7264397Z         >>> input = torch.randn(2, 50, names=('N', 'features'))
2025-03-14T04:33:23.7264615Z         >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
2025-03-14T04:33:23.7264727Z         >>> output = unflatten(input)
2025-03-14T04:33:23.7264839Z         >>> output.size()
2025-03-14T04:33:23.7264940Z         torch.Size([2, 2, 5, 5])
2025-03-14T04:33:23.7265035Z     
2025-03-14T04:33:23.7265287Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7265386Z     
2025-03-14T04:33:23.7265489Z       warnings.warn(msg)
2025-03-14T04:33:23.7265589Z     
2025-03-14T04:33:23.7265787Z --- Parse Warning: 93 / 116 ---
2025-03-14T04:33:23.7266766Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1700.
2025-03-14T04:33:23.7267032Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7267274Z     Creates a criterion that measures the triplet loss given input
2025-03-14T04:33:23.7267475Z         tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2025-03-14T04:33:23.7267701Z         positive, and negative examples, respectively), and a nonnegative,
2025-03-14T04:33:23.7267953Z         real-valued function ("distance function") used to compute the relationship
2025-03-14T04:33:23.7268191Z         between the anchor and positive example ("positive distance") and the
2025-03-14T04:33:23.7268352Z         anchor and negative example ("negative distance").
2025-03-14T04:33:23.7268449Z     
2025-03-14T04:33:23.7268660Z         The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2025-03-14T04:33:23.7268777Z         can be described as:
2025-03-14T04:33:23.7268863Z     
2025-03-14T04:33:23.7268971Z         .. math::
2025-03-14T04:33:23.7269114Z             \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2025-03-14T04:33:23.7269285Z             l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2025-03-14T04:33:23.7269374Z     
2025-03-14T04:33:23.7269636Z         where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2025-03-14T04:33:23.7269926Z         quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2025-03-14T04:33:23.7270179Z         and :math:`margin` is a nonnegative margin representing the minimum difference
2025-03-14T04:33:23.7270451Z         between the positive and negative distances that is required for the loss to
2025-03-14T04:33:23.7270716Z         be 0.  The input tensors have :math:`N` elements each and can be of any shape
2025-03-14T04:33:23.7270844Z         that the distance function can handle.
2025-03-14T04:33:23.7270942Z     
2025-03-14T04:33:23.7271062Z         If :attr:`reduction` is not ``'none'``
2025-03-14T04:33:23.7271184Z         (default ``'mean'``), then:
2025-03-14T04:33:23.7271274Z     
2025-03-14T04:33:23.7271381Z         .. math::
2025-03-14T04:33:23.7271478Z             \ell(x, y) =
2025-03-14T04:33:23.7271588Z             \begin{cases}
2025-03-14T04:33:23.7271794Z                 \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2025-03-14T04:33:23.7272001Z                 \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2025-03-14T04:33:23.7272095Z             \end{cases}
2025-03-14T04:33:23.7272192Z     
2025-03-14T04:33:23.7272428Z         See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2025-03-14T04:33:23.7272693Z         loss for input tensors using the :math:`l_p` distance as the distance function.
2025-03-14T04:33:23.7272780Z     
2025-03-14T04:33:23.7272883Z         Args:
2025-03-14T04:33:23.7273181Z             distance_function (Callable, optional): A nonnegative, real-valued function that
2025-03-14T04:33:23.7273386Z                 quantifies the closeness of two tensors. If not specified,
2025-03-14T04:33:23.7273559Z                 `nn.PairwiseDistance` will be used.  Default: ``None``
2025-03-14T04:33:23.7273842Z             margin (float, optional): A nonnegative margin representing the minimum difference
2025-03-14T04:33:23.7274112Z                 between the positive and negative distances required for the loss to be 0. Larger
2025-03-14T04:33:23.7274403Z                 margins penalize cases where the negative examples are not distant enough from the
2025-03-14T04:33:23.7274580Z                 anchors, relative to the positives. Default: :math:`1`.
2025-03-14T04:33:23.7274844Z             swap (bool, optional): Whether to use the distance swap described in the paper
2025-03-14T04:33:23.7275108Z                 `Learning shallow convolutional feature descriptors with triplet losses` by
2025-03-14T04:33:23.7275358Z                 V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2025-03-14T04:33:23.7275625Z                 negative example than the anchor is, swaps the positive example and the anchor in
2025-03-14T04:33:23.7275800Z                 the loss computation. Default: ``False``.
2025-03-14T04:33:23.7276079Z             reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2025-03-14T04:33:23.7276279Z                 ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-03-14T04:33:23.7276463Z                 ``'mean'``: the sum of the output will be divided by the number of
2025-03-14T04:33:23.7276710Z                 elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2025-03-14T04:33:23.7276799Z     
2025-03-14T04:33:23.7276895Z     
2025-03-14T04:33:23.7276985Z         Shape:
2025-03-14T04:33:23.7277237Z             - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2025-03-14T04:33:23.7277375Z               as supported by the distance function.
2025-03-14T04:33:23.7277639Z             - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2025-03-14T04:33:23.7277735Z               otherwise.
2025-03-14T04:33:23.7277835Z     
2025-03-14T04:33:23.7277932Z         Examples::
2025-03-14T04:33:23.7278029Z     
2025-03-14T04:33:23.7278138Z         >>> # Initialize embeddings
2025-03-14T04:33:23.7278275Z         >>> embedding = nn.Embedding(1000, 128)
2025-03-14T04:33:23.7278406Z         >>> anchor_ids = torch.randint(0, 1000, (1,))
2025-03-14T04:33:23.7278557Z         >>> positive_ids = torch.randint(0, 1000, (1,))
2025-03-14T04:33:23.7278690Z         >>> negative_ids = torch.randint(0, 1000, (1,))
2025-03-14T04:33:23.7278869Z         >>> anchor = embedding(anchor_ids)
2025-03-14T04:33:23.7278995Z         >>> positive = embedding(positive_ids)
2025-03-14T04:33:23.7279216Z         >>> negative = embedding(negative_ids)
2025-03-14T04:33:23.7279320Z         >>>
2025-03-14T04:33:23.7279435Z         >>> # Built-in Distance Function
2025-03-14T04:33:23.7279553Z         >>> triplet_loss = \
2025-03-14T04:33:23.7279834Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2025-03-14T04:33:23.7280008Z         >>> output = triplet_loss(anchor, positive, negative)
2025-03-14T04:33:23.7280113Z         >>> output.backward()
2025-03-14T04:33:23.7280215Z         >>>
2025-03-14T04:33:23.7280328Z         >>> # Custom Distance Function
2025-03-14T04:33:23.7280449Z         >>> def l_infinity(x1, x2):
2025-03-14T04:33:23.7280609Z         >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2025-03-14T04:33:23.7280706Z         >>>
2025-03-14T04:33:23.7280900Z         >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2025-03-14T04:33:23.7281015Z         >>> triplet_loss = (
2025-03-14T04:33:23.7281299Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2025-03-14T04:33:23.7281496Z         >>> output = triplet_loss(anchor, positive, negative)
2025-03-14T04:33:23.7281603Z         >>> output.backward()
2025-03-14T04:33:23.7281702Z         >>>
2025-03-14T04:33:23.7281825Z         >>> # Custom Distance Function (Lambda)
2025-03-14T04:33:23.7281940Z         >>> triplet_loss = (
2025-03-14T04:33:23.7282076Z         >>>     nn.TripletMarginWithDistanceLoss(
2025-03-14T04:33:23.7282307Z         >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2025-03-14T04:33:23.7282463Z         >>> output = triplet_loss(anchor, positive, negative)
2025-03-14T04:33:23.7282582Z         >>> output.backward()
2025-03-14T04:33:23.7282667Z     
2025-03-14T04:33:23.7282773Z         Reference:
2025-03-14T04:33:23.7283082Z             V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2025-03-14T04:33:23.7283322Z             https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html
2025-03-14T04:33:23.7283413Z         
2025-03-14T04:33:23.7283682Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2025-03-14T04:33:23.7283769Z     
2025-03-14T04:33:23.7283885Z       warnings.warn(msg)
2025-03-14T04:33:23.7283971Z     
2025-03-14T04:33:23.7284225Z --- Parse Warning: 94 / 116 ---
2025-03-14T04:33:23.7285108Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395.
2025-03-14T04:33:23.7285388Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7285547Z     Computes a partial inverse of :class:`MaxPool2d`.
2025-03-14T04:33:23.7285646Z     
2025-03-14T04:33:23.7285912Z         :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2025-03-14T04:33:23.7286010Z     
2025-03-14T04:33:23.7286242Z         :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2025-03-14T04:33:23.7286497Z         including the indices of the maximal values and computes a partial inverse
2025-03-14T04:33:23.7286648Z         in which all non-maximal values are set to zero.
2025-03-14T04:33:23.7286746Z     
2025-03-14T04:33:23.7286838Z         Note:
2025-03-14T04:33:23.7287171Z             This operation may behave nondeterministically when the input indices has repeat values.
2025-03-14T04:33:23.7287550Z             See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2025-03-14T04:33:23.7287648Z     
2025-03-14T04:33:23.7287880Z         .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2025-03-14T04:33:23.7288098Z                   sizes. Hence, the inversion process can get ambiguous.
2025-03-14T04:33:23.7288317Z                   To accommodate this, you can provide the needed output size
2025-03-14T04:33:23.7288545Z                   as an additional argument :attr:`output_size` in the forward call.
2025-03-14T04:33:23.7288675Z                   See the Inputs and Example below.
2025-03-14T04:33:23.7288778Z     
2025-03-14T04:33:23.7288869Z         Args:
2025-03-14T04:33:23.7289070Z             kernel_size (int or tuple): Size of the max pooling window.
2025-03-14T04:33:23.7289245Z             stride (int or tuple): Stride of the max pooling window.
2025-03-14T04:33:23.7289398Z                 It is set to :attr:`kernel_size` by default.
2025-03-14T04:33:23.7289583Z             padding (int or tuple): Padding that was added to the input
2025-03-14T04:33:23.7289683Z     
2025-03-14T04:33:23.7289774Z         Inputs:
2025-03-14T04:33:23.7289903Z             - `input`: the input Tensor to invert
2025-03-14T04:33:23.7290131Z             - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2025-03-14T04:33:23.7290296Z             - `output_size` (optional): the targeted output size
2025-03-14T04:33:23.7290424Z     
2025-03-14T04:33:23.7290514Z         Shape:
2025-03-14T04:33:23.7290719Z             - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2025-03-14T04:33:23.7290935Z             - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2025-03-14T04:33:23.7291038Z     
2025-03-14T04:33:23.7291137Z               .. math::
2025-03-14T04:33:23.7291426Z                 H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2025-03-14T04:33:23.7291515Z     
2025-03-14T04:33:23.7291628Z               .. math::
2025-03-14T04:33:23.7291888Z                 W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2025-03-14T04:33:23.7291990Z     
2025-03-14T04:33:23.7292159Z               or as given by :attr:`output_size` in the call operator
2025-03-14T04:33:23.7292262Z     
2025-03-14T04:33:23.7292359Z         Example::
2025-03-14T04:33:23.7292460Z     
2025-03-14T04:33:23.7292629Z             >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2025-03-14T04:33:23.7292774Z             >>> unpool = nn.MaxUnpool2d(2, stride=2)
2025-03-14T04:33:23.7292911Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2025-03-14T04:33:23.7293038Z                                         [ 5.,  6.,  7.,  8.],
2025-03-14T04:33:23.7293179Z                                         [ 9., 10., 11., 12.],
2025-03-14T04:33:23.7293308Z                                         [13., 14., 15., 16.]]]])
2025-03-14T04:33:23.7293429Z             >>> output, indices = pool(input)
2025-03-14T04:33:23.7293553Z             >>> unpool(output, indices)
2025-03-14T04:33:23.7293663Z             tensor([[[[  0.,   0.,   0.,   0.],
2025-03-14T04:33:23.7293784Z                       [  0.,   6.,   0.,   8.],
2025-03-14T04:33:23.7293892Z                       [  0.,   0.,   0.,   0.],
2025-03-14T04:33:23.7294012Z                       [  0.,  14.,   0.,  16.]]]])
2025-03-14T04:33:23.7294227Z             >>> # Now using output_size to resolve an ambiguous size for the inverse
2025-03-14T04:33:23.7294386Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2025-03-14T04:33:23.7294501Z                                         [ 6.,  7.,  8.,  9., 10.],
2025-03-14T04:33:23.7294631Z                                         [11., 12., 13., 14., 15.],
2025-03-14T04:33:23.7294748Z                                         [16., 17., 18., 19., 20.]]]])
2025-03-14T04:33:23.7294881Z             >>> output, indices = pool(input)
2025-03-14T04:33:23.7295055Z             >>> # This call will not work without specifying output_size
2025-03-14T04:33:23.7295226Z             >>> unpool(output, indices, output_size=input.size())
2025-03-14T04:33:23.7295337Z             tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2025-03-14T04:33:23.7295499Z                       [ 0.,  7.,  0.,  9.,  0.],
2025-03-14T04:33:23.7295629Z                       [ 0.,  0.,  0.,  0.,  0.],
2025-03-14T04:33:23.7295745Z                       [ 0., 17.,  0., 19.,  0.]]]])
2025-03-14T04:33:23.7295833Z     
2025-03-14T04:33:23.7295932Z     
2025-03-14T04:33:23.7296020Z         
2025-03-14T04:33:23.7296292Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7296378Z     
2025-03-14T04:33:23.7296494Z       warnings.warn(msg)
2025-03-14T04:33:23.7296579Z     
2025-03-14T04:33:23.7296777Z --- Parse Warning: 95 / 116 ---
2025-03-14T04:33:23.7297666Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270.
2025-03-14T04:33:23.7297943Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7298260Z     Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2025-03-14T04:33:23.7298361Z     
2025-03-14T04:33:23.7298683Z         For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2025-03-14T04:33:23.7298842Z         and with 2D inputs, this class
2025-03-14T04:33:23.7298928Z     
2025-03-14T04:33:23.7299245Z             * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2025-03-14T04:33:23.7299558Z             * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2025-03-14T04:33:23.7299865Z             * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2025-03-14T04:33:23.7299951Z     
2025-03-14T04:33:23.7300311Z         However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2025-03-14T04:33:23.7300405Z         operations.
2025-03-14T04:33:23.7300504Z     
2025-03-14T04:33:23.7300761Z         EmbeddingBag also supports per-sample weights as an argument to the forward
2025-03-14T04:33:23.7301013Z         pass. This scales the output of the Embedding before performing a weighted
2025-03-14T04:33:23.7301263Z         reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2025-03-14T04:33:23.7301512Z         only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2025-03-14T04:33:23.7301649Z         :attr:`per_sample_weights`.
2025-03-14T04:33:23.7301747Z     
2025-03-14T04:33:23.7301837Z         Args:
2025-03-14T04:33:23.7302024Z             num_embeddings (int): size of the dictionary of embeddings
2025-03-14T04:33:23.7302205Z             embedding_dim (int): the size of each embedding vector
2025-03-14T04:33:23.7302518Z             max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2025-03-14T04:33:23.7302690Z                                         is renormalized to have norm :attr:`max_norm`.
2025-03-14T04:33:23.7303041Z             norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2025-03-14T04:33:23.7303366Z             scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2025-03-14T04:33:23.7303545Z                                                     the words in the mini-batch. Default ``False``.
2025-03-14T04:33:23.7303734Z                                                     Note: this option is not supported when ``mode="max"``.
2025-03-14T04:33:23.7303999Z             mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2025-03-14T04:33:23.7304224Z                                      ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2025-03-14T04:33:23.7304462Z                                      into consideration. ``"mean"`` computes the average of the values
2025-03-14T04:33:23.7304694Z                                      in the bag, ``"max"`` computes the max value over each bag.
2025-03-14T04:33:23.7304829Z                                      Default: ``"mean"``
2025-03-14T04:33:23.7305152Z             sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2025-03-14T04:33:23.7305422Z                                      Notes for more details regarding sparse gradients. Note: this option is not
2025-03-14T04:33:23.7305559Z                                      supported when ``mode="max"``.
2025-03-14T04:33:23.7305942Z             include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2025-03-14T04:33:23.7306169Z                                           is equivalent to the size of `indices`. This matches the CSR format.
2025-03-14T04:33:23.7306511Z             padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2025-03-14T04:33:23.7306792Z                                          gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2025-03-14T04:33:23.7307074Z                                          during training, i.e. it remains as a fixed "pad". For a newly constructed
2025-03-14T04:33:23.7307344Z                                          EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2025-03-14T04:33:23.7307603Z                                          zeros, but can be updated to another value to be used as the padding vector.
2025-03-14T04:33:23.7307851Z                                          Note that the embedding vector at :attr:`padding_idx` is excluded from the
2025-03-14T04:33:23.7307975Z                                          reduction.
2025-03-14T04:33:23.7308059Z     
2025-03-14T04:33:23.7308164Z         Attributes:
2025-03-14T04:33:23.7308485Z             weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2025-03-14T04:33:23.7308643Z                              initialized from :math:`\mathcal{N}(0, 1)`.
2025-03-14T04:33:23.7308729Z     
2025-03-14T04:33:23.7308841Z         Examples::
2025-03-14T04:33:23.7308927Z     
2025-03-14T04:33:23.7309113Z             >>> # an EmbeddingBag module containing 10 tensors of size 3
2025-03-14T04:33:23.7309275Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2025-03-14T04:33:23.7309447Z             >>> # a batch of 2 samples of 4 indices each
2025-03-14T04:33:23.7309639Z             >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2025-03-14T04:33:23.7309804Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-14T04:33:23.7309949Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-14T04:33:23.7310081Z             >>> embedding_sum(input, offsets)
2025-03-14T04:33:23.7310195Z             tensor([[-0.8861, -5.4350, -0.0523],
2025-03-14T04:33:23.7310319Z                     [ 1.1306, -2.5798, -1.0044]])
2025-03-14T04:33:23.7310407Z     
2025-03-14T04:33:23.7310534Z             >>> # Example with padding_idx
2025-03-14T04:33:23.7310745Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2025-03-14T04:33:23.7310943Z             >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2025-03-14T04:33:23.7311094Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-14T04:33:23.7311225Z             >>> embedding_sum(input, offsets)
2025-03-14T04:33:23.7311335Z             tensor([[ 0.0000,  0.0000,  0.0000],
2025-03-14T04:33:23.7311453Z                     [-0.7082,  3.2145, -2.6251]])
2025-03-14T04:33:23.7311537Z     
2025-03-14T04:33:23.7311727Z             >>> # An EmbeddingBag can be loaded from an Embedding like so
2025-03-14T04:33:23.7311884Z             >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2025-03-14T04:33:23.7312056Z             >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2025-03-14T04:33:23.7312219Z                     embedding.weight,
2025-03-14T04:33:23.7312365Z                     padding_idx=embedding.padding_idx,
2025-03-14T04:33:23.7312463Z                     mode='sum')
2025-03-14T04:33:23.7312567Z         
2025-03-14T04:33:23.7312820Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7312923Z     
2025-03-14T04:33:23.7313026Z       warnings.warn(msg)
2025-03-14T04:33:23.7313110Z     
2025-03-14T04:33:23.7313322Z --- Parse Warning: 96 / 116 ---
2025-03-14T04:33:23.7314334Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1742.
2025-03-14T04:33:23.7314596Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7314696Z     
2025-03-14T04:33:23.7314936Z     Context manager for training with uneven inputs across processes in DDP.
2025-03-14T04:33:23.7315038Z     
2025-03-14T04:33:23.7315260Z     This context manager will keep track of already-joined DDP processes,
2025-03-14T04:33:23.7315510Z     and "shadow" the forward and backward passes by inserting collective
2025-03-14T04:33:23.7315744Z     communication operations to match with the ones created by non-joined
2025-03-14T04:33:23.7315989Z     DDP processes. This will ensure each collective call has a corresponding
2025-03-14T04:33:23.7316212Z     call by already-joined DDP processes, preventing hangs or errors that
2025-03-14T04:33:23.7316428Z     would otherwise happen when training with uneven inputs across
2025-03-14T04:33:23.7316665Z     processes. Alternatively, if the flag ``throw_on_early_termination`` is
2025-03-14T04:33:23.7316886Z     specified to be ``True``, all trainers will throw an error once one rank
2025-03-14T04:33:23.7317087Z     runs out of inputs, allowing these errors to be caught and handled
2025-03-14T04:33:23.7317219Z     according to application logic.
2025-03-14T04:33:23.7317304Z     
2025-03-14T04:33:23.7317522Z     Once all DDP processes have joined, the context manager will broadcast
2025-03-14T04:33:23.7317763Z     the model corresponding to the last joined process to all processes to
2025-03-14T04:33:23.7317913Z     ensure the model is the same across all processes
2025-03-14T04:33:23.7318037Z     (which is guaranteed by DDP).
2025-03-14T04:33:23.7318122Z     
2025-03-14T04:33:23.7318368Z     To use this to enable training with uneven inputs across processes,
2025-03-14T04:33:23.7318596Z     simply wrap this context manager around your training loop. No further
2025-03-14T04:33:23.7318788Z     modifications to the model or data loading is required.
2025-03-14T04:33:23.7318873Z     
2025-03-14T04:33:23.7318986Z     .. warning::
2025-03-14T04:33:23.7319280Z         If the model or training loop this context manager is wrapped around
2025-03-14T04:33:23.7319489Z         has additional distributed collective operations, such as
2025-03-14T04:33:23.7319682Z         ``SyncBatchNorm`` in the model's forward pass, then the flag
2025-03-14T04:33:23.7319907Z         ``throw_on_early_termination`` must be enabled. This is because this
2025-03-14T04:33:23.7320125Z         context manager is not aware of non-DDP collective communication.
2025-03-14T04:33:23.7320321Z         This flag will cause all ranks to throw when any one rank
2025-03-14T04:33:23.7320537Z         exhausts inputs, allowing these errors to be caught and recovered
2025-03-14T04:33:23.7320660Z         from across all ranks.
2025-03-14T04:33:23.7320749Z     
2025-03-14T04:33:23.7320853Z     Args:
2025-03-14T04:33:23.7321040Z         divide_by_initial_world_size (bool): If ``True``, will divide
2025-03-14T04:33:23.7321265Z             gradients by the initial ``world_size`` DDP training was launched
2025-03-14T04:33:23.7321438Z             with. If ``False``, will compute the effective world size
2025-03-14T04:33:23.7321673Z             (number of ranks that have not depleted their inputs yet) and
2025-03-14T04:33:23.7321852Z             divide gradients by that during allreduce. Set
2025-03-14T04:33:23.7322046Z             ``divide_by_initial_world_size=True`` to ensure every input
2025-03-14T04:33:23.7322262Z             sample including the uneven inputs have equal weight in terms of
2025-03-14T04:33:23.7322453Z             how much they contribute to the global gradient. This is
2025-03-14T04:33:23.7322632Z             achieved by always dividing the gradient by the initial
2025-03-14T04:33:23.7322842Z             ``world_size`` even when we encounter uneven inputs. If you set
2025-03-14T04:33:23.7323016Z             this to ``False``, we divide the gradient by the remaining
2025-03-14T04:33:23.7323229Z             number of nodes. This ensures parity with training on a smaller
2025-03-14T04:33:23.7323419Z             ``world_size`` although it also means the uneven inputs would
2025-03-14T04:33:23.7323632Z             contribute more towards the global gradient. Typically, you
2025-03-14T04:33:23.7323825Z             would want to set this to ``True`` for cases where the last few
2025-03-14T04:33:23.7324041Z             inputs of your training job are uneven. In extreme cases, where
2025-03-14T04:33:23.7324256Z             there is a large discrepancy in the number of inputs, setting
2025-03-14T04:33:23.7324417Z             this to ``False`` might provide better results.
2025-03-14T04:33:23.7324632Z         enable (bool): Whether to enable uneven input detection or not. Pass
2025-03-14T04:33:23.7324823Z             in ``enable=False`` to disable in cases where you know that
2025-03-14T04:33:23.7325016Z             inputs are even across participating processes. Default is
2025-03-14T04:33:23.7325126Z             ``True``.
2025-03-14T04:33:23.7325311Z         throw_on_early_termination (bool): Whether to throw an error
2025-03-14T04:33:23.7325505Z             or continue training when at least one rank has exhausted
2025-03-14T04:33:23.7325694Z             inputs. If ``True``, will throw upon the first rank reaching end
2025-03-14T04:33:23.7325885Z             of data. If ``False``, will continue training with a smaller
2025-03-14T04:33:23.7326086Z             effective world size until all ranks are joined. Note that if
2025-03-14T04:33:23.7326223Z             this flag is specified, then the flag
2025-03-14T04:33:23.7326399Z             ``divide_by_initial_world_size`` would be ignored. Default
2025-03-14T04:33:23.7326509Z             is ``False``.
2025-03-14T04:33:23.7326640Z     
2025-03-14T04:33:23.7326739Z     
2025-03-14T04:33:23.7326836Z     Example::
2025-03-14T04:33:23.7326933Z     
2025-03-14T04:33:23.7327059Z         >>> # xdoctest: +SKIP("Distributed")
2025-03-14T04:33:23.7327170Z         >>> import torch
2025-03-14T04:33:23.7327294Z         >>> import torch.distributed as dist
2025-03-14T04:33:23.7327400Z         >>> import os
2025-03-14T04:33:23.7327529Z         >>> import torch.multiprocessing as mp
2025-03-14T04:33:23.7327650Z         >>> import torch.nn as nn
2025-03-14T04:33:23.7327761Z         >>> # On each spawned worker
2025-03-14T04:33:23.7327878Z         >>> def worker(rank):
2025-03-14T04:33:23.7328059Z         >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2025-03-14T04:33:23.7328189Z         >>>     torch.cuda.set_device(rank)
2025-03-14T04:33:23.7328330Z         >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2025-03-14T04:33:23.7328524Z         >>>     model = torch.nn.parallel.DistributedDataParallel(
2025-03-14T04:33:23.7328669Z         >>>         model, device_ids=[rank], output_device=rank
2025-03-14T04:33:23.7328774Z         >>>     )
2025-03-14T04:33:23.7328905Z         >>>     # Rank 1 gets one more input than rank 0.
2025-03-14T04:33:23.7329110Z         >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2025-03-14T04:33:23.7329218Z         >>>     with model.join():
2025-03-14T04:33:23.7329338Z         >>>         for _ in range(5):
2025-03-14T04:33:23.7329449Z         >>>             for inp in inputs:
2025-03-14T04:33:23.7329619Z         >>>                 loss = model(inp).sum()
2025-03-14T04:33:23.7329745Z         >>>                 loss.backward()
2025-03-14T04:33:23.7329947Z         >>>     # Without the join() API, the below synchronization will hang
2025-03-14T04:33:23.7330107Z         >>>     # blocking for rank 1's allreduce to complete.
2025-03-14T04:33:23.7330237Z         >>>     torch.cuda.synchronize(device=rank)
2025-03-14T04:33:23.7330335Z     
2025-03-14T04:33:23.7330592Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7330692Z     
2025-03-14T04:33:23.7330798Z       warnings.warn(msg)
2025-03-14T04:33:23.7330895Z     
2025-03-14T04:33:23.7331108Z --- Parse Warning: 97 / 116 ---
2025-03-14T04:33:23.7332186Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2033.
2025-03-14T04:33:23.7332453Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7332582Z     
2025-03-14T04:33:23.7332891Z     Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2025-03-14T04:33:23.7332991Z     
2025-03-14T04:33:23.7333201Z     Registers an optimizer with DDP such that the optimization for a
2025-03-14T04:33:23.7333423Z     parameter will run immediately when that parameter's gradient is
2025-03-14T04:33:23.7333629Z     finished with reduction, instead of waiting for all parameters'
2025-03-14T04:33:23.7333861Z     gradients to finish reduction. This can result in a training speedup
2025-03-14T04:33:23.7334080Z     depending on your workload since the optimizer can run while gradient
2025-03-14T04:33:23.7334325Z     reduction for other parameters are still ongoing. In addition, this has
2025-03-14T04:33:23.7334553Z     the potential to reduce peak memory consumption during training, as it
2025-03-14T04:33:23.7334772Z     only needs to load the per-parameter optimizer states of a single
2025-03-14T04:33:23.7334988Z     parameter at a time, instead of loading all per-parameter optimizer
2025-03-14T04:33:23.7335097Z     states at once.
2025-03-14T04:33:23.7335185Z     
2025-03-14T04:33:23.7335286Z     Args:
2025-03-14T04:33:23.7335483Z         optim (Type): a ``torch.optim.Optimizer`` class to be registered
2025-03-14T04:33:23.7335645Z         as a fused optimizer.
2025-03-14T04:33:23.7335818Z         *args (Sequence[Any]): Arguments to forward to `optim`.
2025-03-14T04:33:23.7336046Z         optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2025-03-14T04:33:23.7336270Z         to optimize, similar to `params` argument of traditional `torch.optim`
2025-03-14T04:33:23.7336487Z         Optimizers. If this is omitted, all DDP model parameters will be
2025-03-14T04:33:23.7336581Z         optimized.
2025-03-14T04:33:23.7336798Z         **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2025-03-14T04:33:23.7336883Z     
2025-03-14T04:33:23.7336993Z     .. warning ::
2025-03-14T04:33:23.7337208Z         _register_fused_optim should only be called once on a DDP instance,
2025-03-14T04:33:23.7337433Z         and registering multiple fused optimizers for the same DDP model
2025-03-14T04:33:23.7337568Z         is not currently supported. Please ping
2025-03-14T04:33:23.7337824Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-14T04:33:23.7337925Z         for your use case.
2025-03-14T04:33:23.7338023Z     
2025-03-14T04:33:23.7338118Z     .. warning ::
2025-03-14T04:33:23.7338326Z         _register_fused_optim and register_comm_hook currently do not
2025-03-14T04:33:23.7338542Z         compose together, meaning that custom DDP communication hooks are
2025-03-14T04:33:23.7338730Z         not supported with overlapped optimizers. Please ping
2025-03-14T04:33:23.7338987Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-14T04:33:23.7339126Z         for your use case.
2025-03-14T04:33:23.7339212Z     
2025-03-14T04:33:23.7339320Z     .. warning ::
2025-03-14T04:33:23.7339547Z         Gradient accumulation and DDP `no_sync` are currently not supported
2025-03-14T04:33:23.7339692Z         with overlapped optimizer. Please ping
2025-03-14T04:33:23.7339918Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-14T04:33:23.7340032Z         for your use case.
2025-03-14T04:33:23.7340118Z     
2025-03-14T04:33:23.7340222Z     Example::
2025-03-14T04:33:23.7340306Z     
2025-03-14T04:33:23.7340454Z         >>> # xdoctest: +SKIP("No rendezvous handler")
2025-03-14T04:33:23.7340752Z         >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2025-03-14T04:33:23.7340962Z         >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2025-03-14T04:33:23.7341057Z         >>> lr = 1e-2
2025-03-14T04:33:23.7341159Z         >>> betas = (0.9, 0.99)
2025-03-14T04:33:23.7341264Z         >>> eps = 1e-6
2025-03-14T04:33:23.7341489Z         >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2025-03-14T04:33:23.7341654Z         >>> # Example with subset of parameters
2025-03-14T04:33:23.7341795Z         >>> params_to_opt = [list(net.parameters())[0]]
2025-03-14T04:33:23.7341918Z         >>> net._register_fused_optim(
2025-03-14T04:33:23.7342152Z         ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2025-03-14T04:33:23.7342251Z         ... )
2025-03-14T04:33:23.7342337Z     
2025-03-14T04:33:23.7342602Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7342690Z     
2025-03-14T04:33:23.7342804Z       warnings.warn(msg)
2025-03-14T04:33:23.7342889Z     
2025-03-14T04:33:23.7343102Z --- Parse Warning: 98 / 116 ---
2025-03-14T04:33:23.7344084Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6.
2025-03-14T04:33:23.7344365Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7344584Z     Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2025-03-14T04:33:23.7344683Z     
2025-03-14T04:33:23.7344990Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-14T04:33:23.7345277Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-14T04:33:23.7345533Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-14T04:33:23.7345855Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-14T04:33:23.7345946Z     
2025-03-14T04:33:23.7346055Z         .. note::
2025-03-14T04:33:23.7346297Z             Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2025-03-14T04:33:23.7346539Z             than the utility function ``convert_conv2d_weight_memory_format``. Any
2025-03-14T04:33:23.7346756Z             layer with 4d weight will be affected by ``model.to``, which does not
2025-03-14T04:33:23.7346998Z             necessarily benefit from conversion to specified ``memory_format``.
2025-03-14T04:33:23.7347228Z             One place we are confident in is that NHWC(channels_last) conversion for
2025-03-14T04:33:23.7347461Z             convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2025-03-14T04:33:23.7347668Z             even in cases where we have to apply permutation to input tensors.
2025-03-14T04:33:23.7347764Z     
2025-03-14T04:33:23.7347993Z             Hence our strategy here is to convert only the weight of convolution to
2025-03-14T04:33:23.7352654Z             channels_last. This ensures that;
2025-03-14T04:33:23.7353086Z             1. Fast convolution kernels will be used, the benefit of which could
2025-03-14T04:33:23.7353349Z             outweigh overhead of permutation (if input is not in the same format).
2025-03-14T04:33:23.7353594Z             2. No unnecessary permutations are applied on layers that do not benefit
2025-03-14T04:33:23.7353728Z             from memory_format conversion.
2025-03-14T04:33:23.7353817Z     
2025-03-14T04:33:23.7354061Z             The optimal case is that, layers between convolution layers are channels
2025-03-14T04:33:23.7354303Z             last compatible. Input tensor would be permuted to channels last when it
2025-03-14T04:33:23.7354535Z             encounters the first convolution layer and stay in that memory format.
2025-03-14T04:33:23.7354789Z             Hence following convolutions will not need to permute its input tensor.
2025-03-14T04:33:23.7354878Z     
2025-03-14T04:33:23.7355115Z             In case where a channels last incompatible layer is between convolution
2025-03-14T04:33:23.7355335Z             layers, we need to permute the input tensor back to contiguous format
2025-03-14T04:33:23.7355573Z             for that layer. The input tensor will go through the remaining layers in
2025-03-14T04:33:23.7355841Z             contiguous format and be permuted to channels last when it encounters
2025-03-14T04:33:23.7356067Z             another convolution layer. There's no point in propagating that
2025-03-14T04:33:23.7356290Z             permutation to an earlier layer, as most layers are quite agnostic to
2025-03-14T04:33:23.7356406Z             ``memory_format``.
2025-03-14T04:33:23.7356494Z     
2025-03-14T04:33:23.7356741Z             This claim might change when PyTorch supports fusion of permutation, as
2025-03-14T04:33:23.7356965Z             there might have been a better spot to fuse the permutation other than
2025-03-14T04:33:23.7357104Z             immediately before a convolution.
2025-03-14T04:33:23.7357193Z     
2025-03-14T04:33:23.7357293Z         Args:
2025-03-14T04:33:23.7357517Z             module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2025-03-14T04:33:23.7357638Z                                 ``nn.Module``
2025-03-14T04:33:23.7357797Z             memory_format: user specified ``memory_format``,
2025-03-14T04:33:23.7358001Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-14T04:33:23.7358087Z     
2025-03-14T04:33:23.7358188Z         Returns:
2025-03-14T04:33:23.7358372Z             The original module with updated ``nn.Conv2d``
2025-03-14T04:33:23.7358471Z     
2025-03-14T04:33:23.7358562Z         Example:
2025-03-14T04:33:23.7358704Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:23.7358876Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-14T04:33:23.7359108Z             >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda")
2025-03-14T04:33:23.7359335Z             >>> model = nn.Sequential(
2025-03-14T04:33:23.7359460Z             >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2025-03-14T04:33:23.7359583Z             >>> # This is identical to:
2025-03-14T04:33:23.7359831Z             >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-14T04:33:23.7360117Z             >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-14T04:33:23.7360223Z             >>> out = model(input)
2025-03-14T04:33:23.7360324Z         
2025-03-14T04:33:23.7360582Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7360680Z     
2025-03-14T04:33:23.7360784Z       warnings.warn(msg)
2025-03-14T04:33:23.7360879Z     
2025-03-14T04:33:23.7361117Z --- Parse Warning: 99 / 116 ---
2025-03-14T04:33:23.7362152Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81.
2025-03-14T04:33:23.7362441Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7362672Z     Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2025-03-14T04:33:23.7362949Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-14T04:33:23.7363235Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-14T04:33:23.7363492Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-14T04:33:23.7363813Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-14T04:33:23.7363901Z     
2025-03-14T04:33:23.7364014Z         .. note::
2025-03-14T04:33:23.7364262Z             Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2025-03-14T04:33:23.7364505Z             than the utility function ``convert_conv3d_weight_memory_format``. Any
2025-03-14T04:33:23.7364720Z             layer with 4d weight will be affected by ``model.to``, which does not
2025-03-14T04:33:23.7365007Z             necessarily benefit from conversion to specified ``memory_format``.
2025-03-14T04:33:23.7365249Z             One place we are confident in is that NDHWC(channels_last_3d) conversion for
2025-03-14T04:33:23.7365487Z             convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2025-03-14T04:33:23.7365696Z             even in cases where we have to apply permutation to input tensors.
2025-03-14T04:33:23.7365797Z     
2025-03-14T04:33:23.7366025Z             Hence our strategy here is to convert only the weight of convolution to
2025-03-14T04:33:23.7366162Z             channels_last_3d. This ensures that;
2025-03-14T04:33:23.7366383Z             1. Fast convolution kernels will be used, the benefit of which could
2025-03-14T04:33:23.7366635Z             outweigh overhead of permutation (if input is not in the same format).
2025-03-14T04:33:23.7366877Z             2. No unnecessary permutations are applied on layers that do not benefit
2025-03-14T04:33:23.7367015Z             from memory_format conversion.
2025-03-14T04:33:23.7367102Z     
2025-03-14T04:33:23.7367347Z             The optimal case is that, layers between convolution layers are channels
2025-03-14T04:33:23.7367584Z             last compatible. Input tensor would be permuted to channels last when it
2025-03-14T04:33:23.7367859Z             encounters the first convolution layer and stay in that memory format.
2025-03-14T04:33:23.7368098Z             Hence following convolutions will not need to permute its input tensor.
2025-03-14T04:33:23.7368196Z     
2025-03-14T04:33:23.7368421Z             In case where a channels last incompatible layer is between convolution
2025-03-14T04:33:23.7368647Z             layers, we need to permute the input tensor back to contiguous format
2025-03-14T04:33:23.7368875Z             for that layer. The input tensor will go through the remaining layers in
2025-03-14T04:33:23.7369115Z             contiguous format and be permuted to channels last when it encounters
2025-03-14T04:33:23.7369331Z             another convolution layer. There's no point in propagating that
2025-03-14T04:33:23.7369565Z             permutation to an earlier layer, as most layers are quite agnostic to
2025-03-14T04:33:23.7369671Z             ``memory_format``.
2025-03-14T04:33:23.7369770Z     
2025-03-14T04:33:23.7370008Z             This claim might change when PyTorch supports fusion of permutation, as
2025-03-14T04:33:23.7370243Z             there might have been a better spot to fuse the permutation other than
2025-03-14T04:33:23.7370365Z             immediately before a convolution.
2025-03-14T04:33:23.7370463Z     
2025-03-14T04:33:23.7370554Z         Args:
2025-03-14T04:33:23.7370785Z             module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2025-03-14T04:33:23.7370893Z                                 ``nn.Module``
2025-03-14T04:33:23.7371116Z             memory_format: user specified ``memory_format``,
2025-03-14T04:33:23.7371303Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-14T04:33:23.7371403Z     
2025-03-14T04:33:23.7371494Z         Returns:
2025-03-14T04:33:23.7371659Z             The original module with updated ``nn.Conv3d``
2025-03-14T04:33:23.7371746Z     
2025-03-14T04:33:23.7371852Z         Example:
2025-03-14T04:33:23.7371997Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-14T04:33:23.7372171Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-14T04:33:23.7372407Z             >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda")
2025-03-14T04:33:23.7372533Z             >>> model = nn.Sequential(
2025-03-14T04:33:23.7372653Z             >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2025-03-14T04:33:23.7372777Z             >>> # This is identical to:
2025-03-14T04:33:23.7373038Z             >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-14T04:33:23.7373325Z             >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-14T04:33:23.7373455Z             >>> out = model(input)
2025-03-14T04:33:23.7373553Z         
2025-03-14T04:33:23.7373809Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7373905Z     
2025-03-14T04:33:23.7374010Z       warnings.warn(msg)
2025-03-14T04:33:23.7374108Z     
2025-03-14T04:33:23.7374312Z --- Parse Warning: 100 / 116 ---
2025-03-14T04:33:23.7375203Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=935.
2025-03-14T04:33:23.7375466Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7375714Z     Prune tensor by removing random channels along the specified dimension.
2025-03-14T04:33:23.7375800Z     
2025-03-14T04:33:23.7376050Z         Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-14T04:33:23.7376270Z         by removing the specified ``amount`` of (currently unpruned) channels
2025-03-14T04:33:23.7376430Z         along the specified ``dim`` selected at random.
2025-03-14T04:33:23.7376656Z         Modifies module in place (and also return the modified module)
2025-03-14T04:33:23.7376754Z         by:
2025-03-14T04:33:23.7376840Z     
2025-03-14T04:33:23.7377060Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:23.7377280Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:23.7377502Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:23.7377713Z            original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:23.7377833Z            ``name+'_orig'``.
2025-03-14T04:33:23.7377917Z     
2025-03-14T04:33:23.7378017Z         Args:
2025-03-14T04:33:23.7378198Z             module (nn.Module): module containing the tensor to prune
2025-03-14T04:33:23.7378397Z             name (str): parameter name within ``module`` on which pruning
2025-03-14T04:33:23.7378495Z                     will act.
2025-03-14T04:33:23.7378682Z             amount (int or float): quantity of parameters to prune.
2025-03-14T04:33:23.7378862Z                 If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-14T04:33:23.7379081Z                 fraction of parameters to prune. If ``int``, it represents the
2025-03-14T04:33:23.7379218Z                 absolute number of parameters to prune.
2025-03-14T04:33:23.7379438Z             dim (int): index of the dim along which we define channels to prune.
2025-03-14T04:33:23.7379522Z     
2025-03-14T04:33:23.7379625Z         Returns:
2025-03-14T04:33:23.7379878Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-14T04:33:23.7379990Z     
2025-03-14T04:33:23.7380095Z         Examples:
2025-03-14T04:33:23.7380201Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7380331Z             >>> m = prune.random_structured(
2025-03-14T04:33:23.7380475Z             ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
2025-03-14T04:33:23.7380580Z             ... )
2025-03-14T04:33:23.7380766Z             >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
2025-03-14T04:33:23.7380890Z             >>> print(columns_pruned)
2025-03-14T04:33:23.7380978Z             3
2025-03-14T04:33:23.7381079Z         
2025-03-14T04:33:23.7381334Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7381433Z     
2025-03-14T04:33:23.7381536Z       warnings.warn(msg)
2025-03-14T04:33:23.7381632Z     
2025-03-14T04:33:23.7381831Z --- Parse Warning: 101 / 116 ---
2025-03-14T04:33:23.7382699Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=976.
2025-03-14T04:33:23.7382994Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7383317Z     Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2025-03-14T04:33:23.7383405Z     
2025-03-14T04:33:23.7383664Z         Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-14T04:33:23.7383886Z         by removing the specified ``amount`` of (currently unpruned) channels
2025-03-14T04:33:23.7384083Z         along the specified ``dim`` with the lowest L\ ``n``-norm.
2025-03-14T04:33:23.7384283Z         Modifies module in place (and also return the modified module)
2025-03-14T04:33:23.7384385Z         by:
2025-03-14T04:33:23.7384468Z     
2025-03-14T04:33:23.7384693Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:23.7384917Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:23.7385141Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:23.7385352Z            original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:23.7385465Z            ``name+'_orig'``.
2025-03-14T04:33:23.7385551Z     
2025-03-14T04:33:23.7385652Z         Args:
2025-03-14T04:33:23.7385858Z             module (nn.Module): module containing the tensor to prune
2025-03-14T04:33:23.7386057Z             name (str): parameter name within ``module`` on which pruning
2025-03-14T04:33:23.7386156Z                     will act.
2025-03-14T04:33:23.7386342Z             amount (int or float): quantity of parameters to prune.
2025-03-14T04:33:23.7386519Z                 If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-14T04:33:23.7386741Z                 fraction of parameters to prune. If ``int``, it represents the
2025-03-14T04:33:23.7386879Z                 absolute number of parameters to prune.
2025-03-14T04:33:23.7387091Z             n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2025-03-14T04:33:23.7387251Z                 entries for argument ``p`` in :func:`torch.norm`.
2025-03-14T04:33:23.7387467Z             dim (int): index of the dim along which we define channels to prune.
2025-03-14T04:33:23.7387706Z             importance_scores (torch.Tensor): tensor of importance scores (of same
2025-03-14T04:33:23.7387913Z                 shape as module parameter) used to compute mask for pruning.
2025-03-14T04:33:23.7388139Z                 The values in this tensor indicate the importance of the corresponding
2025-03-14T04:33:23.7388289Z                 elements in the parameter being pruned.
2025-03-14T04:33:23.7388519Z                 If unspecified or None, the module parameter will be used in its place.
2025-03-14T04:33:23.7388645Z     
2025-03-14T04:33:23.7388759Z         Returns:
2025-03-14T04:33:23.7388993Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-14T04:33:23.7389080Z     
2025-03-14T04:33:23.7389184Z         Examples:
2025-03-14T04:33:23.7389308Z             >>> from torch.nn.utils import prune
2025-03-14T04:33:23.7389438Z             >>> m = prune.ln_structured(
2025-03-14T04:33:23.7389626Z             ...     nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf')
2025-03-14T04:33:23.7389729Z             ... )
2025-03-14T04:33:23.7389815Z         
2025-03-14T04:33:23.7390084Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7390167Z     
2025-03-14T04:33:23.7390269Z       warnings.warn(msg)
2025-03-14T04:33:23.7390364Z     
2025-03-14T04:33:23.7390563Z --- Parse Warning: 102 / 116 ---
2025-03-14T04:33:23.7391467Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1023.
2025-03-14T04:33:23.7391771Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7391854Z     
2025-03-14T04:33:23.7392296Z     Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2025-03-14T04:33:23.7392382Z     
2025-03-14T04:33:23.7392499Z     Modifies modules in place by:
2025-03-14T04:33:23.7392592Z     
2025-03-14T04:33:23.7392804Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:23.7393034Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:23.7393246Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:23.7393467Z        original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:23.7393566Z        ``name+'_orig'``.
2025-03-14T04:33:23.7393667Z     
2025-03-14T04:33:23.7393755Z     Args:
2025-03-14T04:33:23.7393974Z         parameters (Iterable of (module, name) tuples): parameters of
2025-03-14T04:33:23.7394169Z             the model to prune in a global fashion, i.e. by aggregating all
2025-03-14T04:33:23.7394392Z             weights prior to deciding which ones to prune. module must be of
2025-03-14T04:33:23.7394550Z             type :class:`nn.Module`, and name must be a string.
2025-03-14T04:33:23.7394813Z         pruning_method (function): a valid pruning function from this module,
2025-03-14T04:33:23.7394990Z             or a custom one implemented by the user that satisfies the
2025-03-14T04:33:23.7395234Z             implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2025-03-14T04:33:23.7395464Z         importance_scores (dict): a dictionary mapping (module, name) tuples to
2025-03-14T04:33:23.7395699Z             the corresponding parameter's importance scores tensor. The tensor
2025-03-14T04:33:23.7395914Z             should be the same shape as the parameter, and is used for computing
2025-03-14T04:33:23.7396031Z             mask for pruning.
2025-03-14T04:33:23.7396245Z             If unspecified or None, the parameter will be used in place of its
2025-03-14T04:33:23.7396360Z             importance scores.
2025-03-14T04:33:23.7396488Z         kwargs: other keyword arguments such as:
2025-03-14T04:33:23.7396702Z             amount (int or float): quantity of parameters to prune across the
2025-03-14T04:33:23.7396812Z             specified parameters.
2025-03-14T04:33:23.7396999Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-14T04:33:23.7397201Z             fraction of parameters to prune. If ``int``, it represents the
2025-03-14T04:33:23.7397347Z             absolute number of parameters to prune.
2025-03-14T04:33:23.7397434Z     
2025-03-14T04:33:23.7397537Z     Raises:
2025-03-14T04:33:23.7397692Z         TypeError: if ``PRUNING_TYPE != 'unstructured'``
2025-03-14T04:33:23.7397857Z     
2025-03-14T04:33:23.7397947Z     Note:
2025-03-14T04:33:23.7398175Z         Since global structured pruning doesn't make much sense unless the
2025-03-14T04:33:23.7398380Z         norm is normalized by the size of the parameter, we now limit the
2025-03-14T04:33:23.7398546Z         scope of global pruning to unstructured methods.
2025-03-14T04:33:23.7398630Z     
2025-03-14T04:33:23.7398737Z     Examples:
2025-03-14T04:33:23.7398862Z         >>> from torch.nn.utils import prune
2025-03-14T04:33:23.7399001Z         >>> from collections import OrderedDict
2025-03-14T04:33:23.7399219Z         >>> net = nn.Sequential(OrderedDict([
2025-03-14T04:33:23.7399352Z         ...     ('first', nn.Linear(10, 4)),
2025-03-14T04:33:23.7399466Z         ...     ('second', nn.Linear(4, 1)),
2025-03-14T04:33:23.7399569Z         ... ]))
2025-03-14T04:33:23.7399681Z         >>> parameters_to_prune = (
2025-03-14T04:33:23.7399802Z         ...     (net.first, 'weight'),
2025-03-14T04:33:23.7399914Z         ...     (net.second, 'weight'),
2025-03-14T04:33:23.7400018Z         ... )
2025-03-14T04:33:23.7400135Z         >>> prune.global_unstructured(
2025-03-14T04:33:23.7400324Z         ...     parameters_to_prune,
2025-03-14T04:33:23.7400464Z         ...     pruning_method=prune.L1Unstructured,
2025-03-14T04:33:23.7400561Z         ...     amount=10,
2025-03-14T04:33:23.7400663Z         ... )
2025-03-14T04:33:23.7400888Z         >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2025-03-14T04:33:23.7400995Z         tensor(10)
2025-03-14T04:33:23.7401082Z     
2025-03-14T04:33:23.7401181Z     
2025-03-14T04:33:23.7401435Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7401534Z     
2025-03-14T04:33:23.7401634Z       warnings.warn(msg)
2025-03-14T04:33:23.7401735Z     
2025-03-14T04:33:23.7401945Z --- Parse Warning: 103 / 116 ---
2025-03-14T04:33:23.7402829Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1142.
2025-03-14T04:33:23.7403094Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7403505Z     Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2025-03-14T04:33:23.7403592Z     
2025-03-14T04:33:23.7403850Z         Modifies module in place (and also return the modified module) by:
2025-03-14T04:33:23.7403933Z     
2025-03-14T04:33:23.7404158Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-14T04:33:23.7404383Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-03-14T04:33:23.7404607Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-03-14T04:33:23.7404816Z            original (unpruned) parameter is stored in a new parameter named
2025-03-14T04:33:23.7404935Z            ``name+'_orig'``.
2025-03-14T04:33:23.7405021Z     
2025-03-14T04:33:23.7405120Z         Args:
2025-03-14T04:33:23.7405302Z             module (nn.Module): module containing the tensor to prune
2025-03-14T04:33:23.7405501Z             name (str): parameter name within ``module`` on which pruning
2025-03-14T04:33:23.7405597Z                 will act.
2025-03-14T04:33:23.7405789Z             mask (Tensor): binary mask to be applied to the parameter.
2025-03-14T04:33:23.7405876Z     
2025-03-14T04:33:23.7405978Z         Returns:
2025-03-14T04:33:23.7406197Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-14T04:33:23.7406295Z     
2025-03-14T04:33:23.7406386Z         Examples:
2025-03-14T04:33:23.7406525Z             >>> from torch.nn.utils import prune
2025-03-14T04:33:23.7406641Z             >>> m = prune.custom_from_mask(
2025-03-14T04:33:23.7406830Z             ...     nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0])
2025-03-14T04:33:23.7407046Z             ... )
2025-03-14T04:33:23.7407167Z             >>> print(m.bias_mask)
2025-03-14T04:33:23.7407265Z             tensor([0., 1., 0.])
2025-03-14T04:33:23.7407367Z     
2025-03-14T04:33:23.7407452Z         
2025-03-14T04:33:23.7407721Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7407807Z     
2025-03-14T04:33:23.7407909Z       warnings.warn(msg)
2025-03-14T04:33:23.7408006Z     
2025-03-14T04:33:23.7408200Z --- Parse Warning: 104 / 116 ---
2025-03-14T04:33:23.7409077Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=117.
2025-03-14T04:33:23.7409340Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7409713Z     Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2025-03-14T04:33:23.7409815Z     
2025-03-14T04:33:23.7410059Z         Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2025-03-14T04:33:23.7410307Z         Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2025-03-14T04:33:23.7410540Z         Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2025-03-14T04:33:23.7410633Z         (UAI 2018).
2025-03-14T04:33:23.7410731Z     
2025-03-14T04:33:23.7410950Z         Exponential Moving Average is a variation of `Polyak averaging`_,
2025-03-14T04:33:23.7411204Z         but using exponential weights instead of equal weights across iterations.
2025-03-14T04:33:23.7411288Z     
2025-03-14T04:33:23.7411538Z         AveragedModel class creates a copy of the provided module :attr:`model`
2025-03-14T04:33:23.7411764Z         on the device :attr:`device` and allows to compute running averages of the
2025-03-14T04:33:23.7411891Z         parameters of the :attr:`model`.
2025-03-14T04:33:23.7411981Z     
2025-03-14T04:33:23.7412081Z         Args:
2025-03-14T04:33:23.7412241Z             model (torch.nn.Module): model to use with SWA/EMA
2025-03-14T04:33:23.7412577Z             device (torch.device, optional): if provided, the averaged model will be
2025-03-14T04:33:23.7412692Z                 stored on the :attr:`device`
2025-03-14T04:33:23.7412911Z             avg_fn (function, optional): the averaging function used to update
2025-03-14T04:33:23.7413140Z                 parameters; the function must take in the current value of the
2025-03-14T04:33:23.7413374Z                 :class:`AveragedModel` parameter, the current value of :attr:`model`
2025-03-14T04:33:23.7413573Z                 parameter, and the number of models already averaged; if None,
2025-03-14T04:33:23.7413747Z                 an equally weighted average is used (default: None)
2025-03-14T04:33:23.7413992Z             multi_avg_fn (function, optional): the averaging function used to update
2025-03-14T04:33:23.7414233Z                 parameters inplace; the function must take in the current values of the
2025-03-14T04:33:23.7414513Z                 :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2025-03-14T04:33:23.7414746Z                 parameters as a list, and the number of models already averaged; if None,
2025-03-14T04:33:23.7414918Z                 an equally weighted average is used (default: None)
2025-03-14T04:33:23.7415125Z             use_buffers (bool): if ``True``, it will compute running averages for
2025-03-14T04:33:23.7415362Z                 both the parameters and the buffers of the model. (default: ``False``)
2025-03-14T04:33:23.7415447Z     
2025-03-14T04:33:23.7415549Z         Example:
2025-03-14T04:33:23.7415687Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.7415830Z             >>> loader, optimizer, model, loss_fn = ...
2025-03-14T04:33:23.7416000Z             >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2025-03-14T04:33:23.7416280Z             >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2025-03-14T04:33:23.7416395Z             >>>                                     T_max=300)
2025-03-14T04:33:23.7416505Z             >>> swa_start = 160
2025-03-14T04:33:23.7416647Z             >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2025-03-14T04:33:23.7416758Z             >>> for i in range(300):
2025-03-14T04:33:23.7416876Z             >>>      for input, target in loader:
2025-03-14T04:33:23.7417007Z             >>>          optimizer.zero_grad()
2025-03-14T04:33:23.7417142Z             >>>          loss_fn(model(input), target).backward()
2025-03-14T04:33:23.7417262Z             >>>          optimizer.step()
2025-03-14T04:33:23.7417361Z             >>>      if i > swa_start:
2025-03-14T04:33:23.7417503Z             >>>          swa_model.update_parameters(model)
2025-03-14T04:33:23.7417615Z             >>>          swa_scheduler.step()
2025-03-14T04:33:23.7417719Z             >>>      else:
2025-03-14T04:33:23.7417828Z             >>>          scheduler.step()
2025-03-14T04:33:23.7417925Z             >>>
2025-03-14T04:33:23.7418079Z             >>> # Update bn statistics for the swa_model at the end
2025-03-14T04:33:23.7418280Z             >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2025-03-14T04:33:23.7418364Z     
2025-03-14T04:33:23.7418672Z         You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2025-03-14T04:33:23.7418869Z         If no averaging function is provided, the default is to compute
2025-03-14T04:33:23.7419030Z         equally-weighted average of the weights (SWA).
2025-03-14T04:33:23.7419112Z     
2025-03-14T04:33:23.7419208Z         Example:
2025-03-14T04:33:23.7419339Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-14T04:33:23.7419556Z             >>> # Compute exponential moving averages of the weights and buffers
2025-03-14T04:33:23.7419727Z             >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2025-03-14T04:33:23.7419961Z             >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2025-03-14T04:33:23.7420045Z     
2025-03-14T04:33:23.7420156Z         .. note::
2025-03-14T04:33:23.7420375Z             When using SWA/EMA with models containing Batch Normalization you may
2025-03-14T04:33:23.7420589Z             need to update the activation statistics for Batch Normalization.
2025-03-14T04:33:23.7420847Z             This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2025-03-14T04:33:23.7421084Z             or by setting :attr:`use_buffers` to `True`. The first approach updates the
2025-03-14T04:33:23.7421325Z             statistics in a post-training step by passing data through the model. The
2025-03-14T04:33:23.7421568Z             second does it during the parameter update phase by averaging all buffers.
2025-03-14T04:33:23.7421815Z             Empirical evidence has shown that updating the statistics in normalization
2025-03-14T04:33:23.7422058Z             layers increases accuracy, but you may wish to empirically test which
2025-03-14T04:33:23.7422217Z             approach yields the best results in your problem.
2025-03-14T04:33:23.7422318Z     
2025-03-14T04:33:23.7422407Z         .. note::
2025-03-14T04:33:23.7422668Z             :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2025-03-14T04:33:23.7422751Z     
2025-03-14T04:33:23.7422850Z         .. note::
2025-03-14T04:33:23.7423048Z             When :meth:`update_parameters` is called for the first time (i.e.
2025-03-14T04:33:23.7423248Z             :attr:`n_averaged` is `0`) the parameters of `model` are copied
2025-03-14T04:33:23.7423448Z             to the parameters of :class:`AveragedModel`. For every subsequent
2025-03-14T04:33:23.7423646Z             call of :meth:`update_parameters` the function `avg_fn` is used
2025-03-14T04:33:23.7423754Z             to update the parameters.
2025-03-14T04:33:23.7423844Z     
2025-03-14T04:33:23.7424112Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-14T04:33:23.7424248Z             https://arxiv.org/abs/1803.05407
2025-03-14T04:33:23.7424488Z         .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2025-03-14T04:33:23.7424586Z             Average:
2025-03-14T04:33:23.7424703Z             https://arxiv.org/abs/1806.05594
2025-03-14T04:33:23.7424917Z         .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2025-03-14T04:33:23.7425037Z             https://arxiv.org/abs/1904.11943
2025-03-14T04:33:23.7425269Z         .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2025-03-14T04:33:23.7425370Z             Generalizes Well:
2025-03-14T04:33:23.7425497Z             https://arxiv.org/abs/2001.02312
2025-03-14T04:33:23.7425597Z         .. _Polyak averaging:
2025-03-14T04:33:23.7425782Z             https://paperswithcode.com/method/polyak-averaging
2025-03-14T04:33:23.7425890Z         
2025-03-14T04:33:23.7426150Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7426231Z     
2025-03-14T04:33:23.7426330Z       warnings.warn(msg)
2025-03-14T04:33:23.7426446Z     
2025-03-14T04:33:23.7426647Z --- Parse Warning: 105 / 116 ---
2025-03-14T04:33:23.7427489Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=369.
2025-03-14T04:33:23.7427757Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7427970Z     Anneals the learning rate in each parameter group to a fixed value.
2025-03-14T04:33:23.7428061Z     
2025-03-14T04:33:23.7428292Z         This learning rate scheduler is meant to be used with Stochastic Weight
2025-03-14T04:33:23.7428500Z         Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2025-03-14T04:33:23.7428593Z     
2025-03-14T04:33:23.7428679Z         Args:
2025-03-14T04:33:23.7428860Z             optimizer (torch.optim.Optimizer): wrapped optimizer
2025-03-14T04:33:23.7429070Z             swa_lrs (float or list): the learning rate value for all param groups
2025-03-14T04:33:23.7429208Z                 together or separately for each group.
2025-03-14T04:33:23.7429408Z             annealing_epochs (int): number of epochs in the annealing phase
2025-03-14T04:33:23.7429515Z                 (default: 10)
2025-03-14T04:33:23.7429753Z             annealing_strategy (str): "cos" or "linear"; specifies the annealing
2025-03-14T04:33:23.7429970Z                 strategy: "cos" for cosine annealing, "linear" for linear annealing
2025-03-14T04:33:23.7430068Z                 (default: "cos")
2025-03-14T04:33:23.7430255Z             last_epoch (int): the index of the last epoch (default: -1)
2025-03-14T04:33:23.7430340Z     
2025-03-14T04:33:23.7430527Z         The :class:`SWALR` scheduler can be used together with other
2025-03-14T04:33:23.7430748Z         schedulers to switch to a constant learning rate late in the training
2025-03-14T04:33:23.7430863Z         as in the example below.
2025-03-14T04:33:23.7430949Z     
2025-03-14T04:33:23.7431046Z         Example:
2025-03-14T04:33:23.7431177Z             >>> # xdoctest: +SKIP("Undefined variables")
2025-03-14T04:33:23.7431311Z             >>> loader, optimizer, model = ...
2025-03-14T04:33:23.7431425Z             >>> lr_lambda = lambda epoch: 0.9
2025-03-14T04:33:23.7431658Z             >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2025-03-14T04:33:23.7431765Z             >>>        lr_lambda=lr_lambda)
2025-03-14T04:33:23.7431948Z             >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2025-03-14T04:33:23.7432128Z             >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2025-03-14T04:33:23.7432241Z             >>> swa_start = 160
2025-03-14T04:33:23.7432341Z             >>> for i in range(300):
2025-03-14T04:33:23.7432530Z             >>>      for input, target in loader:
2025-03-14T04:33:23.7432643Z             >>>          optimizer.zero_grad()
2025-03-14T04:33:23.7432790Z             >>>          loss_fn(model(input), target).backward()
2025-03-14T04:33:23.7432897Z             >>>          optimizer.step()
2025-03-14T04:33:23.7433008Z             >>>      if i > swa_start:
2025-03-14T04:33:23.7433117Z             >>>          swa_scheduler.step()
2025-03-14T04:33:23.7433220Z             >>>      else:
2025-03-14T04:33:23.7433327Z             >>>          scheduler.step()
2025-03-14T04:33:23.7433425Z     
2025-03-14T04:33:23.7433649Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-14T04:33:23.7433784Z             https://arxiv.org/abs/1803.05407
2025-03-14T04:33:23.7433868Z         
2025-03-14T04:33:23.7434128Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7434209Z     
2025-03-14T04:33:23.7434319Z       warnings.warn(msg)
2025-03-14T04:33:23.7434403Z     
2025-03-14T04:33:23.7434609Z --- Parse Warning: 106 / 116 ---
2025-03-14T04:33:23.7435498Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1263.
2025-03-14T04:33:23.7435794Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7435945Z     Asserts that ``actual`` and ``expected`` are close.
2025-03-14T04:33:23.7436037Z     
2025-03-14T04:33:23.7436397Z         If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2025-03-14T04:33:23.7436498Z     
2025-03-14T04:33:23.7436586Z         .. math::
2025-03-14T04:33:23.7436678Z     
2025-03-14T04:33:23.7437032Z             \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2025-03-14T04:33:23.7437128Z     
2025-03-14T04:33:23.7437463Z         Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2025-03-14T04:33:23.7437682Z         only considered equal to each other if ``equal_nan`` is ``True``.
2025-03-14T04:33:23.7437762Z     
2025-03-14T04:33:23.7437961Z         In addition, they are only considered close if they have the same
2025-03-14T04:33:23.7438057Z     
2025-03-14T04:33:23.7438275Z         - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2025-03-14T04:33:23.7438418Z         - ``dtype`` (if ``check_dtype`` is ``True``),
2025-03-14T04:33:23.7438562Z         - ``layout`` (if ``check_layout`` is ``True``), and
2025-03-14T04:33:23.7438697Z         - stride (if ``check_stride`` is ``True``).
2025-03-14T04:33:23.7438777Z     
2025-03-14T04:33:23.7439083Z         If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2025-03-14T04:33:23.7439252Z     
2025-03-14T04:33:23.7439626Z         If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2025-03-14T04:33:23.7439996Z         checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2025-03-14T04:33:23.7440231Z         or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2025-03-14T04:33:23.7440611Z         are always checked for equality whereas the values are checked for closeness according to the definition above.
2025-03-14T04:33:23.7440704Z     
2025-03-14T04:33:23.7440983Z         If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2025-03-14T04:33:23.7441338Z         :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2025-03-14T04:33:23.7441436Z         definition above.
2025-03-14T04:33:23.7441529Z     
2025-03-14T04:33:23.7441860Z         ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2025-03-14T04:33:23.7442264Z         :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2025-03-14T04:33:23.7442636Z         have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2025-03-14T04:33:23.7443009Z         or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2025-03-14T04:33:23.7443246Z         their elements are considered close according to the above definition.
2025-03-14T04:33:23.7443330Z     
2025-03-14T04:33:23.7443434Z         .. note::
2025-03-14T04:33:23.7443517Z     
2025-03-14T04:33:23.7443846Z             Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2025-03-14T04:33:23.7444177Z             :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2025-03-14T04:33:23.7444469Z             Python scalars of different types can be checked, but require ``check_dtype=False``.
2025-03-14T04:33:23.7444581Z     
2025-03-14T04:33:23.7444672Z         Args:
2025-03-14T04:33:23.7444799Z             actual (Any): Actual input.
2025-03-14T04:33:23.7444922Z             expected (Any): Expected input.
2025-03-14T04:33:23.7445295Z             allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2025-03-14T04:33:23.7445458Z                 are allowed. Otherwise type equality is required.
2025-03-14T04:33:23.7445839Z             rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2025-03-14T04:33:23.7446117Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-14T04:33:23.7446477Z             atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2025-03-14T04:33:23.7446751Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-14T04:33:23.7447002Z             equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2025-03-14T04:33:23.7447303Z             check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2025-03-14T04:33:23.7447577Z                 :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2025-03-14T04:33:23.7447827Z                 :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2025-03-14T04:33:23.7448176Z             check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2025-03-14T04:33:23.7448531Z                 check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2025-03-14T04:33:23.7448895Z                 :func:`torch.promote_types`) before being compared.
2025-03-14T04:33:23.7449270Z             check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2025-03-14T04:33:23.7449608Z                 check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2025-03-14T04:33:23.7449727Z                 compared.
2025-03-14T04:33:23.7450095Z             check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2025-03-14T04:33:23.7450465Z             msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2025-03-14T04:33:23.7450825Z                 the comparison. Can also passed as callable in which case it will be called with the generated message and
2025-03-14T04:33:23.7450964Z                 should return the new message.
2025-03-14T04:33:23.7451052Z     
2025-03-14T04:33:23.7451157Z         Raises:
2025-03-14T04:33:23.7451493Z             ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2025-03-14T04:33:23.7451679Z             ValueError: If only ``rtol`` or ``atol`` is specified.
2025-03-14T04:33:23.7452011Z             AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2025-03-14T04:33:23.7452387Z             AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2025-03-14T04:33:23.7452498Z                 different types.
2025-03-14T04:33:23.7452877Z             AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2025-03-14T04:33:23.7453242Z             AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2025-03-14T04:33:23.7453572Z             AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2025-03-14T04:33:23.7453873Z             AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2025-03-14T04:33:23.7454016Z                 :attr:`~torch.Tensor.layout`.
2025-03-14T04:33:23.7454273Z             AssertionError: If only one of corresponding tensors is quantized.
2025-03-14T04:33:23.7454673Z             AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2025-03-14T04:33:23.7454972Z             AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2025-03-14T04:33:23.7455113Z                 :attr:`~torch.Tensor.device`.
2025-03-14T04:33:23.7455449Z             AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2025-03-14T04:33:23.7455823Z             AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2025-03-14T04:33:23.7456188Z             AssertionError: If the values of corresponding tensors are not close according to the definition above.
2025-03-14T04:33:23.7456295Z     
2025-03-14T04:33:23.7456658Z         The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2025-03-14T04:33:23.7456830Z         ``dtype``'s, the maximum of both tolerances is used.
2025-03-14T04:33:23.7456919Z     
2025-03-14T04:33:23.7457072Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7457240Z         | ``dtype``                 | ``rtol``   | ``atol`` |
2025-03-14T04:33:23.7457364Z         +===========================+============+==========+
2025-03-14T04:33:23.7457511Z         | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2025-03-14T04:33:23.7457656Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7457801Z         | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2025-03-14T04:33:23.7457944Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7458085Z         | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:23.7458229Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7458370Z         | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2025-03-14T04:33:23.7458508Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7458648Z         | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2025-03-14T04:33:23.7458787Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7458930Z         | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:23.7459069Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7459210Z         | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2025-03-14T04:33:23.7459349Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7459489Z         | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:23.7459654Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7459819Z         | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:23.7459957Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7460100Z         | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:23.7460240Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7460377Z         | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:23.7460521Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7460659Z         | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2025-03-14T04:33:23.7460805Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7460932Z         | other                     | ``0.0``    | ``0.0``  |
2025-03-14T04:33:23.7461074Z         +---------------------------+------------+----------+
2025-03-14T04:33:23.7461163Z     
2025-03-14T04:33:23.7461268Z         .. note::
2025-03-14T04:33:23.7461358Z     
2025-03-14T04:33:23.7461754Z             :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2025-03-14T04:33:23.7462136Z             to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2025-03-14T04:33:23.7462411Z             define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2025-03-14T04:33:23.7462498Z     
2025-03-14T04:33:23.7462618Z             >>> import functools
2025-03-14T04:33:23.7462881Z             >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2025-03-14T04:33:23.7463011Z             >>> assert_equal(1e-9, 1e-10)
2025-03-14T04:33:23.7463134Z             Traceback (most recent call last):
2025-03-14T04:33:23.7463234Z             ...
2025-03-14T04:33:23.7463366Z             AssertionError: Scalars are not equal!
2025-03-14T04:33:23.7463476Z             <BLANKLINE>
2025-03-14T04:33:23.7463594Z             Expected 1e-10 but got 1e-09.
2025-03-14T04:33:23.7463739Z             Absolute difference: 9.000000000000001e-10
2025-03-14T04:33:23.7463853Z             Relative difference: 9.0
2025-03-14T04:33:23.7463951Z     
2025-03-14T04:33:23.7464043Z         Examples:
2025-03-14T04:33:23.7464174Z             >>> # tensor to tensor comparison
2025-03-14T04:33:23.7464311Z             >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2025-03-14T04:33:23.7464502Z             >>> actual = torch.acos(torch.cos(expected))
2025-03-14T04:33:23.7464655Z             >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:23.7464754Z     
2025-03-14T04:33:23.7464872Z             >>> # scalar to scalar comparison
2025-03-14T04:33:23.7464984Z             >>> import math
2025-03-14T04:33:23.7465098Z             >>> expected = math.sqrt(2.0)
2025-03-14T04:33:23.7465213Z             >>> actual = 2.0 / math.sqrt(2.0)
2025-03-14T04:33:23.7465377Z             >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:23.7465467Z     
2025-03-14T04:33:23.7465612Z             >>> # numpy array to numpy array comparison
2025-03-14T04:33:23.7465722Z             >>> import numpy as np
2025-03-14T04:33:23.7465868Z             >>> expected = np.array([1e0, 1e-1, 1e-2])
2025-03-14T04:33:23.7466000Z             >>> actual = np.arccos(np.cos(expected))
2025-03-14T04:33:23.7466166Z             >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:23.7466253Z     
2025-03-14T04:33:23.7466391Z             >>> # sequence to sequence comparison
2025-03-14T04:33:23.7466498Z             >>> import numpy as np
2025-03-14T04:33:23.7466765Z             >>> # The types of the sequences do not have to match. They only have to have the same
2025-03-14T04:33:23.7466900Z             >>> # length and their elements have to match.
2025-03-14T04:33:23.7467072Z             >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2025-03-14T04:33:23.7467184Z             >>> actual = tuple(expected)
2025-03-14T04:33:23.7467398Z             >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:23.7467484Z     
2025-03-14T04:33:23.7467621Z             >>> # mapping to mapping comparison
2025-03-14T04:33:23.7467751Z             >>> from collections import OrderedDict
2025-03-14T04:33:23.7467871Z             >>> import numpy as np
2025-03-14T04:33:23.7467982Z             >>> foo = torch.tensor(1.0)
2025-03-14T04:33:23.7468090Z             >>> bar = 2.0
2025-03-14T04:33:23.7468195Z             >>> baz = np.array(3.0)
2025-03-14T04:33:23.7468467Z             >>> # The types and a possible ordering of mappings do not have to match. They only
2025-03-14T04:33:23.7468669Z             >>> # have to have the same set of keys and their elements have to match.
2025-03-14T04:33:23.7468892Z             >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2025-03-14T04:33:23.7469031Z             >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2025-03-14T04:33:23.7469194Z             >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:23.7469285Z     
2025-03-14T04:33:23.7469427Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-14T04:33:23.7469571Z             >>> actual = expected.clone()
2025-03-14T04:33:23.7469754Z             >>> # By default, directly related instances can be compared
2025-03-14T04:33:23.7469974Z             >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2025-03-14T04:33:23.7470182Z             >>> # This check can be made more strict with allow_subclasses=False
2025-03-14T04:33:23.7470301Z             >>> torch.testing.assert_close(
2025-03-14T04:33:23.7470516Z             ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2025-03-14T04:33:23.7470604Z             ... )
2025-03-14T04:33:23.7470737Z             Traceback (most recent call last):
2025-03-14T04:33:23.7470826Z             ...
2025-03-14T04:33:23.7471047Z             TypeError: No comparison pair was able to handle inputs of type
2025-03-14T04:33:23.7471272Z             <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2025-03-14T04:33:23.7471512Z             >>> # If the inputs are not directly related, they are never considered close
2025-03-14T04:33:23.7471689Z             >>> torch.testing.assert_close(actual.numpy(), expected)
2025-03-14T04:33:23.7471824Z             Traceback (most recent call last):
2025-03-14T04:33:23.7471913Z             ...
2025-03-14T04:33:23.7472240Z             TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2025-03-14T04:33:23.7472354Z             and <class 'torch.Tensor'>.
2025-03-14T04:33:23.7472630Z             >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2025-03-14T04:33:23.7472755Z             >>> # their type if check_dtype=False.
2025-03-14T04:33:23.7472940Z             >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2025-03-14T04:33:23.7473026Z     
2025-03-14T04:33:23.7473146Z             >>> # NaN != NaN by default.
2025-03-14T04:33:23.7473281Z             >>> expected = torch.tensor(float("Nan"))
2025-03-14T04:33:23.7473410Z             >>> actual = expected.clone()
2025-03-14T04:33:23.7473564Z             >>> torch.testing.assert_close(actual, expected)
2025-03-14T04:33:23.7473698Z             Traceback (most recent call last):
2025-03-14T04:33:23.7473786Z             ...
2025-03-14T04:33:23.7473929Z             AssertionError: Scalars are not close!
2025-03-14T04:33:23.7474024Z             <BLANKLINE>
2025-03-14T04:33:23.7474142Z             Expected nan but got nan.
2025-03-14T04:33:23.7474305Z             Absolute difference: nan (up to 1e-05 allowed)
2025-03-14T04:33:23.7474459Z             Relative difference: nan (up to 1.3e-06 allowed)
2025-03-14T04:33:23.7474675Z             >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2025-03-14T04:33:23.7474765Z     
2025-03-14T04:33:23.7474908Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-14T04:33:23.7475059Z             >>> actual = torch.tensor([1.0, 4.0, 5.0])
2025-03-14T04:33:23.7475247Z             >>> # The default error message can be overwritten.
2025-03-14T04:33:23.7475537Z             >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
2025-03-14T04:33:23.7475675Z             Traceback (most recent call last):
2025-03-14T04:33:23.7475763Z             ...
2025-03-14T04:33:23.7475932Z             AssertionError: Argh, the tensors are not close!
2025-03-14T04:33:23.7476159Z             >>> # If msg is a callable, it can be used to augment the generated message with
2025-03-14T04:33:23.7476276Z             >>> # extra information
2025-03-14T04:33:23.7476395Z             >>> torch.testing.assert_close(
2025-03-14T04:33:23.7476611Z             ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2025-03-14T04:33:23.7476700Z             ... )
2025-03-14T04:33:23.7476834Z             Traceback (most recent call last):
2025-03-14T04:33:23.7476922Z             ...
2025-03-14T04:33:23.7477051Z             AssertionError: Header
2025-03-14T04:33:23.7477147Z             <BLANKLINE>
2025-03-14T04:33:23.7477268Z             Tensor-likes are not close!
2025-03-14T04:33:23.7477391Z             <BLANKLINE>
2025-03-14T04:33:23.7477516Z             Mismatched elements: 2 / 3 (66.7%)
2025-03-14T04:33:23.7477747Z             Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2025-03-14T04:33:23.7477988Z             Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2025-03-14T04:33:23.7478083Z             <BLANKLINE>
2025-03-14T04:33:23.7478185Z             Footer
2025-03-14T04:33:23.7478271Z         
2025-03-14T04:33:23.7478533Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7478619Z     
2025-03-14T04:33:23.7478731Z       warnings.warn(msg)
2025-03-14T04:33:23.7478817Z     
2025-03-14T04:33:23.7479062Z --- Parse Warning: 107 / 116 ---
2025-03-14T04:33:23.7480072Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=104.
2025-03-14T04:33:23.7480350Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7480503Z     Register a container-like type as pytree node.
2025-03-14T04:33:23.7480603Z     
2025-03-14T04:33:23.7480694Z         Args:
2025-03-14T04:33:23.7480925Z             cls (type): A Python type to treat as an internal pytree node.
2025-03-14T04:33:23.7481201Z             flatten_fn (callable): A function to be used during flattening, taking an instance of
2025-03-14T04:33:23.7481467Z                 ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2025-03-14T04:33:23.7481757Z                 recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2025-03-14T04:33:23.7481894Z                 passed to the ``unflatten_fn``.
2025-03-14T04:33:23.7482179Z             unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2025-03-14T04:33:23.7482456Z                 returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2025-03-14T04:33:23.7482616Z                 The function should return an instance of ``cls``.
2025-03-14T04:33:23.7482893Z             serialized_type_name (str, optional): A keyword argument used to specify the fully
2025-03-14T04:33:23.7483064Z                 qualified name used when serializing the tree spec.
2025-03-14T04:33:23.7483385Z             to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2025-03-14T04:33:23.7483665Z                 to convert the context of the pytree to a custom json dumpable representation. This is
2025-03-14T04:33:23.7483946Z                 used for json serialization, which is being used in :mod:`torch.export` right now.
2025-03-14T04:33:23.7484270Z             from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2025-03-14T04:33:23.7484573Z                 how to convert the custom json dumpable representation of the context back to the
2025-03-14T04:33:23.7484833Z                 original context. This is used for json deserialization, which is being used in
2025-03-14T04:33:23.7484964Z                 :mod:`torch.export` right now.
2025-03-14T04:33:23.7485046Z     
2025-03-14T04:33:23.7485158Z         Example::
2025-03-14T04:33:23.7485247Z     
2025-03-14T04:33:23.7485361Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7485514Z             >>> # Registry a Python type with lambda functions
2025-03-14T04:33:23.7485631Z             >>> register_pytree_node(
2025-03-14T04:33:23.7485723Z             ...     set,
2025-03-14T04:33:23.7485860Z             ...     lambda s: (sorted(s), None, None),
2025-03-14T04:33:23.7485991Z             ...     lambda children, _: set(children),
2025-03-14T04:33:23.7486086Z             ... )
2025-03-14T04:33:23.7486174Z         
2025-03-14T04:33:23.7486440Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7486550Z     
2025-03-14T04:33:23.7486662Z       warnings.warn(msg)
2025-03-14T04:33:23.7486743Z     
2025-03-14T04:33:23.7486962Z --- Parse Warning: 108 / 116 ---
2025-03-14T04:33:23.7487919Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1200.
2025-03-14T04:33:23.7488190Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7488271Z     
2025-03-14T04:33:23.7488501Z     Context passed to policy function during selective checkpointing.
2025-03-14T04:33:23.7488585Z     
2025-03-14T04:33:23.7488820Z     This class is used to pass relevant metadata to the policy function during
2025-03-14T04:33:23.7489086Z     selective checkpointing. The metadata includes whether the current invocation
2025-03-14T04:33:23.7489265Z     of the policy function is during recomputation or not.
2025-03-14T04:33:23.7489350Z     
2025-03-14T04:33:23.7489452Z     Example:
2025-03-14T04:33:23.7489558Z         >>> # xdoctest: +SKIP(stub)
2025-03-14T04:33:23.7489645Z         >>>
2025-03-14T04:33:23.7489789Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-14T04:33:23.7489927Z         >>>    print(ctx.is_recompute)
2025-03-14T04:33:23.7490023Z         >>>
2025-03-14T04:33:23.7490298Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-14T04:33:23.7490395Z         >>>
2025-03-14T04:33:23.7490544Z         >>> out = torch.utils.checkpoint.checkpoint(
2025-03-14T04:33:23.7490651Z         >>>     fn, x, y,
2025-03-14T04:33:23.7490760Z         >>>     use_reentrant=False,
2025-03-14T04:33:23.7490877Z         >>>     context_fn=context_fn,
2025-03-14T04:33:23.7490964Z         >>> )
2025-03-14T04:33:23.7491062Z     
2025-03-14T04:33:23.7491317Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7491413Z     
2025-03-14T04:33:23.7491513Z       warnings.warn(msg)
2025-03-14T04:33:23.7491609Z     
2025-03-14T04:33:23.7491800Z --- Parse Warning: 109 / 116 ---
2025-03-14T04:33:23.7492791Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1334.
2025-03-14T04:33:23.7493055Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7493149Z     
2025-03-14T04:33:23.7493387Z     Helper to avoid recomputing certain ops during activation checkpointing.
2025-03-14T04:33:23.7493480Z     
2025-03-14T04:33:23.7493697Z     Use this with `torch.utils.checkpoint.checkpoint` to control which
2025-03-14T04:33:23.7493920Z     operations are recomputed during the backward pass.
2025-03-14T04:33:23.7494006Z     
2025-03-14T04:33:23.7494104Z     Args:
2025-03-14T04:33:23.7494231Z         policy_fn_or_list (Callable or List):
2025-03-14T04:33:23.7494410Z           - If a policy function is provided, it should accept a
2025-03-14T04:33:23.7494656Z             :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2025-03-14T04:33:23.7494875Z             kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2025-03-14T04:33:23.7495116Z             indicating whether the execution of the op should be recomputed or not.
2025-03-14T04:33:23.7495331Z           - If a list of operations is provided, it is equivalent to a policy
2025-03-14T04:33:23.7495517Z             returning `CheckpointPolicy.MUST_SAVE` for the specified
2025-03-14T04:33:23.7495746Z             operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2025-03-14T04:33:23.7495843Z             operations.
2025-03-14T04:33:23.7496073Z         allow_cache_entry_mutation (bool, optional): By default, an error is
2025-03-14T04:33:23.7496286Z             raised if any tensors cached by selective activation checkpoint are
2025-03-14T04:33:23.7496547Z             mutated in order to ensure correctness. If set to `True`, this check
2025-03-14T04:33:23.7496644Z             is disabled.
2025-03-14T04:33:23.7496743Z     Returns:
2025-03-14T04:33:23.7496859Z         A tuple of two context managers.
2025-03-14T04:33:23.7496955Z     
2025-03-14T04:33:23.7497043Z     Example:
2025-03-14T04:33:23.7497165Z         >>> # xdoctest: +REQUIRES(LINUX)
2025-03-14T04:33:23.7497265Z         >>> import functools
2025-03-14T04:33:23.7497351Z         >>>
2025-03-14T04:33:23.7497488Z         >>> x = torch.rand(10, 10, requires_grad=True)
2025-03-14T04:33:23.7497616Z         >>> y = torch.rand(10, 10, requires_grad=True)
2025-03-14T04:33:23.7497715Z         >>>
2025-03-14T04:33:23.7497815Z         >>> ops_to_save = [
2025-03-14T04:33:23.7497949Z         >>>    torch.ops.aten.mm.default,
2025-03-14T04:33:23.7498036Z         >>> ]
2025-03-14T04:33:23.7498132Z         >>>
2025-03-14T04:33:23.7498265Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-14T04:33:23.7498382Z         >>>    if op in ops_to_save:
2025-03-14T04:33:23.7498514Z         >>>        return CheckpointPolicy.MUST_SAVE
2025-03-14T04:33:23.7498613Z         >>>    else:
2025-03-14T04:33:23.7498760Z         >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2025-03-14T04:33:23.7498879Z         >>>
2025-03-14T04:33:23.7499153Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-14T04:33:23.7499248Z         >>>
2025-03-14T04:33:23.7499354Z         >>> # or equivalently
2025-03-14T04:33:23.7499635Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2025-03-14T04:33:23.7499722Z         >>>
2025-03-14T04:33:23.7499828Z         >>> def fn(x, y):
2025-03-14T04:33:23.7500032Z         >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2025-03-14T04:33:23.7500130Z         >>>
2025-03-14T04:33:23.7500274Z         >>> out = torch.utils.checkpoint.checkpoint(
2025-03-14T04:33:23.7500381Z         >>>     fn, x, y,
2025-03-14T04:33:23.7500486Z         >>>     use_reentrant=False,
2025-03-14T04:33:23.7500606Z         >>>     context_fn=context_fn,
2025-03-14T04:33:23.7500692Z         >>> )
2025-03-14T04:33:23.7500785Z     
2025-03-14T04:33:23.7501040Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7501136Z     
2025-03-14T04:33:23.7501236Z       warnings.warn(msg)
2025-03-14T04:33:23.7501320Z     
2025-03-14T04:33:23.7501524Z --- Parse Warning: 110 / 116 ---
2025-03-14T04:33:23.7502420Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1064.
2025-03-14T04:33:23.7502743Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7502836Z     
2025-03-14T04:33:23.7502984Z     Create a :class:`setuptools.Extension` for C++.
2025-03-14T04:33:23.7503070Z     
2025-03-14T04:33:23.7503325Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-14T04:33:23.7503548Z     bare minimum (but often sufficient) arguments to build a C++ extension.
2025-03-14T04:33:23.7503639Z     
2025-03-14T04:33:23.7503852Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-14T04:33:23.7504014Z     constructor. Full list arguments can be found at
2025-03-14T04:33:23.7504343Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-14T04:33:23.7504440Z     
2025-03-14T04:33:23.7504537Z     .. warning::
2025-03-14T04:33:23.7504776Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-14T04:33:23.7504993Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-14T04:33:23.7505198Z         the user's responsibility in their library to not use APIs from
2025-03-14T04:33:23.7505456Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-14T04:33:23.7505679Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-14T04:33:23.7505895Z         example, to give access to custom ops from python, the library should
2025-03-14T04:33:23.7506035Z         register the ops through the dispatcher.
2025-03-14T04:33:23.7506120Z     
2025-03-14T04:33:23.7506353Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-14T04:33:23.7506559Z         as a compile flag when py_limited_api is specified as an option for
2025-03-14T04:33:23.7506774Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-14T04:33:23.7506991Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-14T04:33:23.7507217Z         safety, and sanity in order to encourage best practices. To target a
2025-03-14T04:33:23.7507432Z         different version, set min_supported_cpython to the hexcode of the
2025-03-14T04:33:23.7507554Z         CPython version of choice.
2025-03-14T04:33:23.7507640Z     
2025-03-14T04:33:23.7507739Z     Example:
2025-03-14T04:33:23.7507843Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7508034Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:23.7508154Z         >>> from setuptools import setup
2025-03-14T04:33:23.7508386Z         >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2025-03-14T04:33:23.7508478Z         >>> setup(
2025-03-14T04:33:23.7508593Z         ...     name='extension',
2025-03-14T04:33:23.7508693Z         ...     ext_modules=[
2025-03-14T04:33:23.7508806Z         ...         CppExtension(
2025-03-14T04:33:23.7508915Z         ...             name='extension',
2025-03-14T04:33:23.7509060Z         ...             sources=['extension.cpp'],
2025-03-14T04:33:23.7509184Z         ...             extra_compile_args=['-g'],
2025-03-14T04:33:23.7509351Z         ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2025-03-14T04:33:23.7509440Z         ...     ],
2025-03-14T04:33:23.7509553Z         ...     cmdclass={
2025-03-14T04:33:23.7509674Z         ...         'build_ext': BuildExtension
2025-03-14T04:33:23.7509771Z         ...     })
2025-03-14T04:33:23.7509861Z     
2025-03-14T04:33:23.7510127Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7510216Z     
2025-03-14T04:33:23.7510331Z       warnings.warn(msg)
2025-03-14T04:33:23.7510417Z     
2025-03-14T04:33:23.7510613Z --- Parse Warning: 111 / 116 ---
2025-03-14T04:33:23.7511545Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1134.
2025-03-14T04:33:23.7511848Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7511937Z     
2025-03-14T04:33:23.7512116Z     Create a :class:`setuptools.Extension` for CUDA/C++.
2025-03-14T04:33:23.7512202Z     
2025-03-14T04:33:23.7512497Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-14T04:33:23.7512704Z     bare minimum (but often sufficient) arguments to build a CUDA/C++
2025-03-14T04:33:23.7512956Z     extension. This includes the CUDA include path, library path and runtime
2025-03-14T04:33:23.7513049Z     library.
2025-03-14T04:33:23.7513137Z     
2025-03-14T04:33:23.7513358Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-14T04:33:23.7513512Z     constructor. Full list arguments can be found at
2025-03-14T04:33:23.7513850Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-14T04:33:23.7513939Z     
2025-03-14T04:33:23.7514047Z     .. warning::
2025-03-14T04:33:23.7514279Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-14T04:33:23.7514524Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-14T04:33:23.7514728Z         the user's responsibility in their library to not use APIs from
2025-03-14T04:33:23.7514973Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-14T04:33:23.7515195Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-14T04:33:23.7515418Z         example, to give access to custom ops from python, the library should
2025-03-14T04:33:23.7515547Z         register the ops through the dispatcher.
2025-03-14T04:33:23.7515640Z     
2025-03-14T04:33:23.7515865Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-14T04:33:23.7516081Z         as a compile flag when py_limited_api is specified as an option for
2025-03-14T04:33:23.7516296Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-14T04:33:23.7516521Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-14T04:33:23.7516738Z         safety, and sanity in order to encourage best practices. To target a
2025-03-14T04:33:23.7516961Z         different version, set min_supported_cpython to the hexcode of the
2025-03-14T04:33:23.7517070Z         CPython version of choice.
2025-03-14T04:33:23.7517191Z     
2025-03-14T04:33:23.7517283Z     Example:
2025-03-14T04:33:23.7517394Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7517548Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:23.7517679Z         >>> from setuptools import setup
2025-03-14T04:33:23.7517904Z         >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2025-03-14T04:33:23.7518003Z         >>> setup(
2025-03-14T04:33:23.7518115Z         ...     name='cuda_extension',
2025-03-14T04:33:23.7518225Z         ...     ext_modules=[
2025-03-14T04:33:23.7518332Z         ...         CUDAExtension(
2025-03-14T04:33:23.7518462Z         ...                 name='cuda_extension',
2025-03-14T04:33:23.7518632Z         ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-14T04:33:23.7518775Z         ...                 extra_compile_args={'cxx': ['-g'],
2025-03-14T04:33:23.7518900Z         ...                                     'nvcc': ['-O2']},
2025-03-14T04:33:23.7519069Z         ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2025-03-14T04:33:23.7519240Z         ...     ],
2025-03-14T04:33:23.7519347Z         ...     cmdclass={
2025-03-14T04:33:23.7519467Z         ...         'build_ext': BuildExtension
2025-03-14T04:33:23.7519569Z         ...     })
2025-03-14T04:33:23.7519653Z     
2025-03-14T04:33:23.7519771Z     Compute capabilities:
2025-03-14T04:33:23.7519857Z     
2025-03-14T04:33:23.7520198Z     By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-14T04:33:23.7520515Z     building process of the extension, plus PTX. If down the road a new card is installed the
2025-03-14T04:33:23.7520820Z     extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2025-03-14T04:33:23.7521124Z     newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
2025-03-14T04:33:23.7521418Z     will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2025-03-14T04:33:23.7521543Z     support (see below for details on PTX).
2025-03-14T04:33:23.7521637Z     
2025-03-14T04:33:23.7521948Z     You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2025-03-14T04:33:23.7522081Z     CCs you want the extension to support:
2025-03-14T04:33:23.7522165Z     
2025-03-14T04:33:23.7522365Z     ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2025-03-14T04:33:23.7522606Z     ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2025-03-14T04:33:23.7522702Z     
2025-03-14T04:33:23.7523022Z     The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2025-03-14T04:33:23.7523369Z     CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2025-03-14T04:33:23.7523670Z     the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2025-03-14T04:33:23.7523976Z     CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2025-03-14T04:33:23.7524300Z     provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2025-03-14T04:33:23.7524582Z     those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2025-03-14T04:33:23.7524906Z     off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2025-03-14T04:33:23.7525231Z     "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2025-03-14T04:33:23.7525333Z     "8.0 8.6" would be better.
2025-03-14T04:33:23.7525429Z     
2025-03-14T04:33:23.7525729Z     Note that while it's possible to include all supported archs, the more archs get included the
2025-03-14T04:33:23.7526029Z     slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-14T04:33:23.7526113Z     
2025-03-14T04:33:23.7526478Z     Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2025-03-14T04:33:23.7526694Z     To workaround the issue, move python binding logic to pure C++ file.
2025-03-14T04:33:23.7526788Z     
2025-03-14T04:33:23.7526882Z     Example use:
2025-03-14T04:33:23.7526995Z         #include <ATen/ATen.h>
2025-03-14T04:33:23.7527154Z         at::Tensor SigmoidAlphaBlendForwardCuda(....)
2025-03-14T04:33:23.7527246Z     
2025-03-14T04:33:23.7527339Z     Instead of:
2025-03-14T04:33:23.7527465Z         #include <torch/extension.h>
2025-03-14T04:33:23.7527626Z         torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2025-03-14T04:33:23.7527709Z     
2025-03-14T04:33:23.7527993Z     Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2025-03-14T04:33:23.7528497Z     Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2025-03-14T04:33:23.7528591Z     
2025-03-14T04:33:23.7528706Z     Relocatable device code linking:
2025-03-14T04:33:23.7528802Z     
2025-03-14T04:33:23.7529080Z     If you want to reference device symbols across compilation units (across object files),
2025-03-14T04:33:23.7529350Z     the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2025-03-14T04:33:23.7529707Z     An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2025-03-14T04:33:23.7530073Z     `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2025-03-14T04:33:23.7530441Z     Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2025-03-14T04:33:23.7530628Z     helps reduce the protentional perf degradation of `-rdc`.
2025-03-14T04:33:23.7530805Z     Note that it needs to be used at both steps to be useful.
2025-03-14T04:33:23.7530890Z     
2025-03-14T04:33:23.7531256Z     If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2025-03-14T04:33:23.7531446Z     There is also a case where `-dlink` is used without `-rdc`:
2025-03-14T04:33:23.7531714Z     when an extension is linked against a static lib containing rdc-compiled objects
2025-03-14T04:33:23.7531927Z     like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2025-03-14T04:33:23.7532010Z     
2025-03-14T04:33:23.7532226Z     Note: Ninja is required to build a CUDA Extension with RDC linking.
2025-03-14T04:33:23.7532313Z     
2025-03-14T04:33:23.7532416Z     Example:
2025-03-14T04:33:23.7532516Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7532710Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:23.7532812Z         >>> CUDAExtension(
2025-03-14T04:33:23.7532936Z         ...        name='cuda_extension',
2025-03-14T04:33:23.7533098Z         ...        sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-14T04:33:23.7533209Z         ...        dlink=True,
2025-03-14T04:33:23.7533330Z         ...        dlink_libraries=["dlink_lib"],
2025-03-14T04:33:23.7533470Z         ...        extra_compile_args={'cxx': ['-g'],
2025-03-14T04:33:23.7533595Z         ...                            'nvcc': ['-O2', '-rdc=true']})
2025-03-14T04:33:23.7533694Z     
2025-03-14T04:33:23.7533941Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7534037Z     
2025-03-14T04:33:23.7534137Z       warnings.warn(msg)
2025-03-14T04:33:23.7534235Z     
2025-03-14T04:33:23.7534450Z --- Parse Warning: 112 / 116 ---
2025-03-14T04:33:23.7535368Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1325.
2025-03-14T04:33:23.7535629Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7535727Z     
2025-03-14T04:33:23.7535917Z     Creates a :class:`setuptools.Extension` for SYCL/C++.
2025-03-14T04:33:23.7536014Z     
2025-03-14T04:33:23.7536255Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-14T04:33:23.7536464Z     bare minimum (but often sufficient) arguments to build a SYCL/C++
2025-03-14T04:33:23.7536554Z     extension.
2025-03-14T04:33:23.7536650Z     
2025-03-14T04:33:23.7536854Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-14T04:33:23.7536962Z     constructor.
2025-03-14T04:33:23.7537043Z     
2025-03-14T04:33:23.7537147Z     .. warning::
2025-03-14T04:33:23.7537376Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-14T04:33:23.7537599Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-14T04:33:23.7537795Z         the user's responsibility in their library to not use APIs from
2025-03-14T04:33:23.7538039Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-14T04:33:23.7538255Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-14T04:33:23.7538479Z         example, to give access to custom ops from python, the library should
2025-03-14T04:33:23.7538609Z         register the ops through the dispatcher.
2025-03-14T04:33:23.7538703Z     
2025-03-14T04:33:23.7538926Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-14T04:33:23.7539168Z         as a compile flag when py_limited_api is specified as an option for
2025-03-14T04:33:23.7539407Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-14T04:33:23.7539636Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-14T04:33:23.7539851Z         safety, and sanity in order to encourage best practices. To target a
2025-03-14T04:33:23.7540081Z         different version, set min_supported_cpython to the hexcode of the
2025-03-14T04:33:23.7540194Z         CPython version of choice.
2025-03-14T04:33:23.7540293Z     
2025-03-14T04:33:23.7540384Z     Example:
2025-03-14T04:33:23.7540501Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7540656Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:23.7540898Z         >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension
2025-03-14T04:33:23.7540992Z         >>> setup(
2025-03-14T04:33:23.7541106Z         ...     name='xpu_extension',
2025-03-14T04:33:23.7541220Z         ...     ext_modules=[
2025-03-14T04:33:23.7541327Z         ...     SyclExtension(
2025-03-14T04:33:23.7541462Z         ...                 name='xpu_extension',
2025-03-14T04:33:23.7541660Z         ...                 sources=['extension.cpp', 'extension_kernel.cpp'],
2025-03-14T04:33:23.7541861Z         ...                 extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
2025-03-14T04:33:23.7541953Z         ...     ],
2025-03-14T04:33:23.7542066Z         ...     cmdclass={
2025-03-14T04:33:23.7542190Z         ...         'build_ext': BuildExtension
2025-03-14T04:33:23.7542294Z         ...     })
2025-03-14T04:33:23.7542382Z     
2025-03-14T04:33:23.7542699Z     By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-14T04:33:23.7542957Z     building process of the extension. If down the road a new card is installed the
2025-03-14T04:33:23.7543228Z     extension may need to be recompiled. You can override the default behavior using
2025-03-14T04:33:23.7543537Z     `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension
2025-03-14T04:33:23.7543646Z     to support:
2025-03-14T04:33:23.7543736Z     
2025-03-14T04:33:23.7543953Z     ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py``
2025-03-14T04:33:23.7544041Z     
2025-03-14T04:33:23.7544354Z     Note that while it's possible to include all supported archs, the more archs get included the
2025-03-14T04:33:23.7544735Z     slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-14T04:33:23.7544835Z     
2025-03-14T04:33:23.7544982Z     Note: Ninja is required to build SyclExtension.
2025-03-14T04:33:23.7545079Z     
2025-03-14T04:33:23.7545333Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7545430Z     
2025-03-14T04:33:23.7545534Z       warnings.warn(msg)
2025-03-14T04:33:23.7545633Z     
2025-03-14T04:33:23.7545832Z --- Parse Warning: 113 / 116 ---
2025-03-14T04:33:23.7546698Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1502.
2025-03-14T04:33:23.7546965Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7547062Z     
2025-03-14T04:33:23.7547211Z     Load a PyTorch C++ extension just-in-time (JIT).
2025-03-14T04:33:23.7547305Z     
2025-03-14T04:33:23.7547513Z     To load an extension, a Ninja build file is emitted, which is used to
2025-03-14T04:33:23.7547732Z     compile the given sources into a dynamic library. This library is
2025-03-14T04:33:23.7547954Z     subsequently loaded into the current Python process as a module and
2025-03-14T04:33:23.7548099Z     returned from this function, ready for use.
2025-03-14T04:33:23.7548183Z     
2025-03-14T04:33:23.7548402Z     By default, the directory to which the build file is emitted and the
2025-03-14T04:33:23.7548934Z     resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2025-03-14T04:33:23.7549160Z     ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2025-03-14T04:33:23.7549380Z     the name of the extension. This location can be overridden in two ways.
2025-03-14T04:33:23.7549603Z     First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2025-03-14T04:33:23.7549823Z     replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2025-03-14T04:33:23.7550056Z     into subfolders of this directory. Second, if the ``build_directory``
2025-03-14T04:33:23.7550291Z     argument to this function is supplied, it overrides the entire path, i.e.
2025-03-14T04:33:23.7550472Z     the library will be compiled into that folder directly.
2025-03-14T04:33:23.7550558Z     
2025-03-14T04:33:23.7550784Z     To compile the sources, the default system compiler (``c++``) is used,
2025-03-14T04:33:23.7551028Z     which can be overridden by setting the ``CXX`` environment variable. To pass
2025-03-14T04:33:23.7551273Z     additional arguments to the compilation process, ``extra_cflags`` or
2025-03-14T04:33:23.7551532Z     ``extra_ldflags`` can be provided. For example, to compile your extension
2025-03-14T04:33:23.7551760Z     with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2025-03-14T04:33:23.7551918Z     ``extra_cflags`` to pass further include directories.
2025-03-14T04:33:23.7552014Z     
2025-03-14T04:33:23.7552254Z     CUDA support with mixed compilation is provided. Simply pass CUDA source
2025-03-14T04:33:23.7552454Z     files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2025-03-14T04:33:23.7552693Z     detected and compiled with nvcc rather than the C++ compiler. This includes
2025-03-14T04:33:23.7552918Z     passing the CUDA lib64 directory as a library directory, and linking
2025-03-14T04:33:23.7553074Z     ``cudart``. You can pass additional flags to nvcc via
2025-03-14T04:33:23.7553296Z     ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2025-03-14T04:33:23.7553529Z     heuristics for finding the CUDA install directory are used, which usually
2025-03-14T04:33:23.7553755Z     work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2025-03-14T04:33:23.7553854Z     safest option.
2025-03-14T04:33:23.7553950Z     
2025-03-14T04:33:23.7554186Z     SYCL support with mixed compilation is provided. Simply pass SYCL source
2025-03-14T04:33:23.7554433Z     files (``.sycl``) along with other sources. Such files will be detected
2025-03-14T04:33:23.7554653Z     and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather
2025-03-14T04:33:23.7554875Z     than the C++ compiler. You can pass additional flags to SYCL compiler
2025-03-14T04:33:23.7555068Z     via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++.
2025-03-14T04:33:23.7555288Z     SYCL compiler is expected to be found via system PATH environment
2025-03-14T04:33:23.7555377Z     variable.
2025-03-14T04:33:23.7555476Z     
2025-03-14T04:33:23.7555564Z     Args:
2025-03-14T04:33:23.7555788Z         name: The name of the extension to build. This MUST be the same as the
2025-03-14T04:33:23.7555905Z             name of the pybind11 module!
2025-03-14T04:33:23.7556109Z         sources: A list of relative or absolute paths to C++ source files.
2025-03-14T04:33:23.7556346Z         extra_cflags: optional list of compiler flags to forward to the build.
2025-03-14T04:33:23.7556584Z         extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2025-03-14T04:33:23.7556697Z             when building CUDA sources.
2025-03-14T04:33:23.7556916Z         extra_sycl_cflags: optional list of compiler flags to forward to SYCL
2025-03-14T04:33:23.7557053Z             compiler when building SYCL sources.
2025-03-14T04:33:23.7557273Z         extra_ldflags: optional list of linker flags to forward to the build.
2025-03-14T04:33:23.7557529Z         extra_include_paths: optional list of include directories to forward
2025-03-14T04:33:23.7557658Z             to the build.
2025-03-14T04:33:23.7557854Z         build_directory: optional path to use as build workspace.
2025-03-14T04:33:23.7558040Z         verbose: If ``True``, turns on verbose logging of load steps.
2025-03-14T04:33:23.7558275Z         with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-14T04:33:23.7558439Z             the build. If set to ``None`` (default), this value is
2025-03-14T04:33:23.7558658Z             automatically determined based on the existence of ``.cu`` or
2025-03-14T04:33:23.7558831Z             ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2025-03-14T04:33:23.7558957Z             and libraries to be included.
2025-03-14T04:33:23.7559249Z         with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-14T04:33:23.7559430Z             the build. If set to ``None`` (default), this value is
2025-03-14T04:33:23.7559648Z             automatically determined based on the existence of ``.sycl`` in
2025-03-14T04:33:23.7559828Z             ``sources``. Set it to `True`` to force SYCL headers and
2025-03-14T04:33:23.7559972Z             libraries to be included.
2025-03-14T04:33:23.7560194Z         is_python_module: If ``True`` (default), imports the produced shared
2025-03-14T04:33:23.7560387Z             library as a Python module. If ``False``, behavior depends on
2025-03-14T04:33:23.7560506Z             ``is_standalone``.
2025-03-14T04:33:23.7560713Z         is_standalone: If ``False`` (default) loads the constructed extension
2025-03-14T04:33:23.7560927Z             into the process as a plain dynamic library. If ``True``, build a
2025-03-14T04:33:23.7561036Z             standalone executable.
2025-03-14T04:33:23.7561136Z     
2025-03-14T04:33:23.7561225Z     Returns:
2025-03-14T04:33:23.7561355Z         If ``is_python_module`` is ``True``:
2025-03-14T04:33:23.7561536Z             Returns the loaded PyTorch extension as a Python module.
2025-03-14T04:33:23.7561634Z     
2025-03-14T04:33:23.7561840Z         If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2025-03-14T04:33:23.7562067Z             Returns nothing. (The shared library is loaded into the process as
2025-03-14T04:33:23.7562167Z             a side effect.)
2025-03-14T04:33:23.7562265Z     
2025-03-14T04:33:23.7562380Z         If ``is_standalone`` is ``True``.
2025-03-14T04:33:23.7562597Z             Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2025-03-14T04:33:23.7562799Z             added to the PATH environment variable as a side effect.)
2025-03-14T04:33:23.7562895Z     
2025-03-14T04:33:23.7562984Z     Example:
2025-03-14T04:33:23.7563097Z         >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7563239Z         >>> from torch.utils.cpp_extension import load
2025-03-14T04:33:23.7563350Z         >>> module = load(
2025-03-14T04:33:23.7563452Z         ...     name='extension',
2025-03-14T04:33:23.7563626Z         ...     sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-14T04:33:23.7563737Z         ...     extra_cflags=['-O2'],
2025-03-14T04:33:23.7563850Z         ...     verbose=True)
2025-03-14T04:33:23.7563932Z     
2025-03-14T04:33:23.7564199Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7564283Z     
2025-03-14T04:33:23.7564399Z       warnings.warn(msg)
2025-03-14T04:33:23.7564484Z     
2025-03-14T04:33:23.7564722Z --- Parse Warning: 114 / 116 ---
2025-03-14T04:33:23.7565605Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1811.
2025-03-14T04:33:23.7565883Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7565968Z     
2025-03-14T04:33:23.7566191Z     Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2025-03-14T04:33:23.7566274Z     
2025-03-14T04:33:23.7566589Z     This function behaves exactly like :func:`load`, but takes its sources as
2025-03-14T04:33:23.7566825Z     strings rather than filenames. These strings are stored to files in the
2025-03-14T04:33:23.7567051Z     build directory, after which the behavior of :func:`load_inline` is
2025-03-14T04:33:23.7567158Z     identical to :func:`load`.
2025-03-14T04:33:23.7567257Z     
2025-03-14T04:33:23.7567347Z     See `the
2025-03-14T04:33:23.7567674Z     tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2025-03-14T04:33:23.7567819Z     for good examples of using this function.
2025-03-14T04:33:23.7567908Z     
2025-03-14T04:33:23.7568155Z     Sources may omit two required parts of a typical non-inline C++ extension:
2025-03-14T04:33:23.7568398Z     the necessary header includes, as well as the (pybind11) binding code. More
2025-03-14T04:33:23.7568653Z     precisely, strings passed to ``cpp_sources`` are first concatenated into a
2025-03-14T04:33:23.7568850Z     single ``.cpp`` file. This file is then prepended with ``#include
2025-03-14T04:33:23.7568968Z     <torch/extension.h>``.
2025-03-14T04:33:23.7569051Z     
2025-03-14T04:33:23.7569316Z     Furthermore, if the ``functions`` argument is supplied, bindings will be
2025-03-14T04:33:23.7569555Z     automatically generated for each function specified. ``functions`` can
2025-03-14T04:33:23.7569788Z     either be a list of function names, or a dictionary mapping from function
2025-03-14T04:33:23.7570013Z     names to docstrings. If a list is given, the name of each function is used
2025-03-14T04:33:23.7570122Z     as its docstring.
2025-03-14T04:33:23.7570208Z     
2025-03-14T04:33:23.7570435Z     The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2025-03-14T04:33:23.7570611Z     file and  prepended with ``torch/types.h``, ``cuda.h`` and
2025-03-14T04:33:23.7570835Z     ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2025-03-14T04:33:23.7571061Z     separately, but ultimately linked into a single library. Note that no
2025-03-14T04:33:23.7571304Z     bindings are generated for functions in ``cuda_sources`` per se. To bind
2025-03-14T04:33:23.7571523Z     to a CUDA kernel, you must create a C++ function that calls it, and either
2025-03-14T04:33:23.7571749Z     declare or define this C++ function in one of the ``cpp_sources`` (and
2025-03-14T04:33:23.7571866Z     include its name in ``functions``).
2025-03-14T04:33:23.7571967Z     
2025-03-14T04:33:23.7572214Z     The sources in ``sycl_sources`` are concatenated into a separate ``.sycl``
2025-03-14T04:33:23.7572450Z     file and  prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes.
2025-03-14T04:33:23.7573034Z     The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately
2025-03-14T04:33:23.7573586Z     linked into a single library. Note that no bindings are generated for
2025-03-14T04:33:23.7574150Z     functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must
2025-03-14T04:33:23.7574711Z     create a C++ function that calls it, and either declare or define this
2025-03-14T04:33:23.7575241Z     C++ function in one of the ``cpp_sources`` (and include its name
2025-03-14T04:33:23.7575653Z     in ``functions``).
2025-03-14T04:33:23.7575918Z     
2025-03-14T04:33:23.7576235Z     See :func:`load` for a description of arguments omitted below.
2025-03-14T04:33:23.7576626Z     
2025-03-14T04:33:23.7576824Z     Args:
2025-03-14T04:33:23.7577178Z         cpp_sources: A string, or list of strings, containing C++ source code.
2025-03-14T04:33:23.7577747Z         cuda_sources: A string, or list of strings, containing CUDA source code.
2025-03-14T04:33:23.7578324Z         sycl_sources: A string, or list of strings, containing SYCL source code.
2025-03-14T04:33:23.7578878Z         functions: A list of function names for which to generate function
2025-03-14T04:33:23.7579428Z             bindings. If a dictionary is given, it should map function names to
2025-03-14T04:33:23.7579988Z             docstrings (which are otherwise just the function names).
2025-03-14T04:33:23.7580608Z         with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-14T04:33:23.7581125Z             the build. If set to ``None`` (default), this value is
2025-03-14T04:33:23.7581617Z             automatically determined based on whether ``cuda_sources`` is
2025-03-14T04:33:23.7582114Z             provided. Set it to ``True`` to force CUDA headers
2025-03-14T04:33:23.7582510Z             and libraries to be included.
2025-03-14T04:33:23.7582960Z         with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-14T04:33:23.7583470Z             the build. If set to ``None`` (default), this value is
2025-03-14T04:33:23.7583955Z             automatically determined based on whether ``sycl_sources`` is
2025-03-14T04:33:23.7584445Z             provided. Set it to ``True`` to force SYCL headers
2025-03-14T04:33:23.7584833Z             and libraries to be included.
2025-03-14T04:33:23.7585270Z         with_pytorch_error_handling: Determines whether pytorch error and
2025-03-14T04:33:23.7585806Z             warning macros are handled by pytorch instead of pybind. To do
2025-03-14T04:33:23.7586377Z             this, each function ``foo`` is called via an intermediary ``_safe_foo``
2025-03-14T04:33:23.7586925Z             function. This redirection might cause issues in obscure cases
2025-03-14T04:33:23.7587440Z             of cpp. This flag should be set to ``False`` when this redirect
2025-03-14T04:33:23.7587856Z             causes issues.
2025-03-14T04:33:23.7588122Z     
2025-03-14T04:33:23.7588333Z     Example:
2025-03-14T04:33:23.7588626Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-14T04:33:23.7589057Z         >>> from torch.utils.cpp_extension import load_inline
2025-03-14T04:33:23.7589422Z         >>> source = """
2025-03-14T04:33:23.7589749Z         at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2025-03-14T04:33:23.7590112Z           return x.sin() + y.sin();
2025-03-14T04:33:23.7590420Z         }
2025-03-14T04:33:23.7590640Z         """
2025-03-14T04:33:23.7590920Z         >>> module = load_inline(name='inline_extension',
2025-03-14T04:33:23.7591308Z         ...                      cpp_sources=[source],
2025-03-14T04:33:23.7591673Z         ...                      functions=['sin_add'])
2025-03-14T04:33:23.7591997Z     
2025-03-14T04:33:23.7592209Z     .. note::
2025-03-14T04:33:23.7592619Z         Since load_inline will just-in-time compile the source code, please ensure
2025-03-14T04:33:23.7593217Z         that you have the right toolchains installed in the runtime. For example,
2025-03-14T04:33:23.7593795Z         when loading C++, make sure a C++ compiler is available. If you're loading
2025-03-14T04:33:23.7594385Z         a CUDA extension, you will need to additionally install the corresponding CUDA
2025-03-14T04:33:23.7595004Z         toolkit (nvcc and any other dependencies your code has). Compiling toolchains
2025-03-14T04:33:23.7595617Z         are not included when you install torch and must be additionally installed.
2025-03-14T04:33:23.7596067Z     
2025-03-14T04:33:23.7596444Z         During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
2025-03-14T04:33:23.7597045Z         the extension. This may use up too many resources on some systems. One
2025-03-14T04:33:23.7597616Z         can control the number of workers by setting the `MAX_JOBS` environment
2025-03-14T04:33:23.7598082Z         variable to a non-negative number.
2025-03-14T04:33:23.7598390Z     
2025-03-14T04:33:23.7598767Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7599319Z     
2025-03-14T04:33:23.7599549Z       warnings.warn(msg)
2025-03-14T04:33:23.7599820Z     
2025-03-14T04:33:23.7600168Z --- Parse Warning: 115 / 116 ---
2025-03-14T04:33:23.7601420Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61.
2025-03-14T04:33:23.7602813Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7603291Z     
2025-03-14T04:33:23.7603716Z     This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2025-03-14T04:33:23.7604222Z     
2025-03-14T04:33:23.7604649Z     This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2025-03-14T04:33:23.7605327Z     for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2025-03-14T04:33:23.7605937Z     server like load. It can emulate multiple calling threads to a single module
2025-03-14T04:33:23.7606540Z     provided. In the future we plan to enhance this component to support inter and
2025-03-14T04:33:23.7607160Z     intra-op parallelism as well as multiple models running in a single process.
2025-03-14T04:33:23.7607622Z     
2025-03-14T04:33:23.7608006Z     Please note that even though nn.Module is supported, it might incur an overhead
2025-03-14T04:33:23.7608614Z     from the need to hold GIL every time we execute Python code or pass around
2025-03-14T04:33:23.7609242Z     inputs as Python objects. As soon as you have a ScriptModule version of your
2025-03-14T04:33:23.7609849Z     model for inference deployment it is better to switch to using it in this
2025-03-14T04:33:23.7610305Z     benchmark.
2025-03-14T04:33:23.7610540Z     
2025-03-14T04:33:23.7610758Z     Example::
2025-03-14T04:33:23.7610986Z     
2025-03-14T04:33:23.7611217Z         >>> # xdoctest: +SKIP("undefined vars")
2025-03-14T04:33:23.7611601Z         >>> from torch.utils import ThroughputBenchmark
2025-03-14T04:33:23.7611998Z         >>> bench = ThroughputBenchmark(my_module)
2025-03-14T04:33:23.7612419Z         >>> # Pre-populate benchmark's data set with the inputs
2025-03-14T04:33:23.7612806Z         >>> for input in inputs:
2025-03-14T04:33:23.7613228Z         ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2025-03-14T04:33:23.7613710Z         ...     bench.add_input(input[0], x2=input[1])
2025-03-14T04:33:23.7614161Z         >>> # Inputs supplied above are randomly used during the execution
2025-03-14T04:33:23.7614589Z         >>> stats = bench.benchmark(
2025-03-14T04:33:23.7614919Z         ...     num_calling_threads=4,
2025-03-14T04:33:23.7615248Z         ...     num_warmup_iters = 100,
2025-03-14T04:33:23.7615581Z         ...     num_iters = 1000,
2025-03-14T04:33:23.7615909Z         ... )
2025-03-14T04:33:23.7616236Z         >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2025-03-14T04:33:23.7616723Z         >>> print("Number of iterations: {}".format(stats.num_iters))
2025-03-14T04:33:23.7617109Z     
2025-03-14T04:33:23.7617489Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7617950Z     
2025-03-14T04:33:23.7618167Z       warnings.warn(msg)
2025-03-14T04:33:23.7618432Z     
2025-03-14T04:33:23.7618762Z --- Parse Warning: 116 / 116 ---
2025-03-14T04:33:23.7619952Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18.
2025-03-14T04:33:23.7621275Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-14T04:33:23.7621869Z     Sampler that restricts data loading to a subset of the dataset.
2025-03-14T04:33:23.7622278Z     
2025-03-14T04:33:23.7622542Z         It is especially useful in conjunction with
2025-03-14T04:33:23.7623049Z         :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2025-03-14T04:33:23.7623684Z         process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2025-03-14T04:33:23.7624302Z         :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2025-03-14T04:33:23.7624856Z         original dataset that is exclusive to it.
2025-03-14T04:33:23.7625194Z     
2025-03-14T04:33:23.7625414Z         .. note::
2025-03-14T04:33:23.7625805Z             Dataset is assumed to be of constant size and that any instance of it always
2025-03-14T04:33:23.7626315Z             returns the same elements in the same order.
2025-03-14T04:33:23.7626660Z     
2025-03-14T04:33:23.7626872Z         Args:
2025-03-14T04:33:23.7627137Z             dataset: Dataset used for sampling.
2025-03-14T04:33:23.7627603Z             num_replicas (int, optional): Number of processes participating in
2025-03-14T04:33:23.7628197Z                 distributed training. By default, :attr:`world_size` is retrieved from the
2025-03-14T04:33:23.7628695Z                 current distributed group.
2025-03-14T04:33:23.7629172Z             rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2025-03-14T04:33:23.7629733Z                 By default, :attr:`rank` is retrieved from the current distributed
2025-03-14T04:33:23.7630158Z                 group.
2025-03-14T04:33:23.7630551Z             shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2025-03-14T04:33:23.7631027Z                 indices.
2025-03-14T04:33:23.7631392Z             seed (int, optional): random seed used to shuffle the sampler if
2025-03-14T04:33:23.7631907Z                 :attr:`shuffle=True`. This number should be identical across all
2025-03-14T04:33:23.7632398Z                 processes in the distributed group. Default: ``0``.
2025-03-14T04:33:23.7632906Z             drop_last (bool, optional): if ``True``, then the sampler will drop the
2025-03-14T04:33:23.7633454Z                 tail of the data to make it evenly divisible across the number of
2025-03-14T04:33:23.7633983Z                 replicas. If ``False``, the sampler will add extra indices to make
2025-03-14T04:33:23.7634524Z                 the data evenly divisible across the replicas. Default: ``False``.
2025-03-14T04:33:23.7634946Z     
2025-03-14T04:33:23.7635171Z         .. warning::
2025-03-14T04:33:23.7635528Z             In distributed mode, calling the :meth:`set_epoch` method at
2025-03-14T04:33:23.7636104Z             the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2025-03-14T04:33:23.7636746Z             is necessary to make shuffling work properly across multiple epochs. Otherwise,
2025-03-14T04:33:23.7637262Z             the same ordering will be always used.
2025-03-14T04:33:23.7637598Z     
2025-03-14T04:33:23.7637874Z         Example::
2025-03-14T04:33:23.7643412Z     
2025-03-14T04:33:23.7643730Z             >>> # xdoctest: +SKIP
2025-03-14T04:33:23.7644186Z             >>> sampler = DistributedSampler(dataset) if is_distributed else None
2025-03-14T04:33:23.7644722Z             >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2025-03-14T04:33:23.7645147Z             ...                     sampler=sampler)
2025-03-14T04:33:23.7645536Z             >>> for epoch in range(start_epoch, n_epochs):
2025-03-14T04:33:23.7645915Z             ...     if is_distributed:
2025-03-14T04:33:23.7646254Z             ...         sampler.set_epoch(epoch)
2025-03-14T04:33:23.7646597Z             ...     train(loader)
2025-03-14T04:33:23.7646872Z         
2025-03-14T04:33:23.7647266Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-14T04:33:23.7647730Z     
2025-03-14T04:33:23.7647958Z       warnings.warn(msg)
2025-03-14T04:33:23.7648219Z     
2025-03-14T04:33:23.7648504Z 
2025-03-14T04:33:23.7648980Z === Found 10 run-time warnings ===
2025-03-14T04:33:23.7649414Z --- Runtime Warning: 1 / 10 ---
2025-03-14T04:33:23.7649920Z example = <DocTest(torch._tensor Tensor.refine_names:0 ln 1347) at 0x7f95fcb04f30>
2025-03-14T04:33:23.7651653Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1365: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ../c10/core/TensorImpl.h:1938.)
2025-03-14T04:33:23.7653158Z       return super().refine_names(names)
2025-03-14T04:33:23.7653480Z     
2025-03-14T04:33:23.7653797Z --- Runtime Warning: 2 / 10 ---
2025-03-14T04:33:23.7654352Z example = <DocTest(torch.library Library._impl_with_aoti_compile:0 ln 251) at 0x7f95fcb06f10>
2025-03-14T04:33:23.7655402Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:288: UserWarning: Warning only once for all operators,  other operators may also be overridden.
2025-03-14T04:33:23.7656445Z       Overriding a previously registered kernel for the same operator and the same dispatch key
2025-03-14T04:33:23.7657093Z       operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor
2025-03-14T04:33:23.7657578Z         registered at aten/src/ATen/RegisterSchema.cpp:6
2025-03-14T04:33:23.7657963Z       dispatch key: CPU
2025-03-14T04:33:23.7658439Z       previous kernel: registered at ../aten/src/ATen/LegacyBatchingRegistrations.cpp:1079
2025-03-14T04:33:23.7659280Z            new kernel: registered at /dev/null:811 (Triggered internally at ../aten/src/ATen/core/dispatch/OperatorEntry.cpp:161.)
2025-03-14T04:33:23.7660029Z       impl_fn(self.ns, name.split("::")[-1], dispatch_key)
2025-03-14T04:33:23.7660394Z     
2025-03-14T04:33:23.7660713Z --- Runtime Warning: 3 / 10 ---
2025-03-14T04:33:23.7661204Z example = <DocTest(torch.nested as_nested_tensor:0 ln 61) at 0x7f95fa185ae0>
2025-03-14T04:33:23.7663244Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py:117: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:182.)
2025-03-14T04:33:23.7665282Z       return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
2025-03-14T04:33:23.7665735Z     
2025-03-14T04:33:23.7666047Z --- Runtime Warning: 4 / 10 ---
2025-03-14T04:33:23.7666549Z example = <DocTest(torch.sparse as_sparse_gradcheck:0 ln 561) at 0x7f95fa186030>
2025-03-14T04:33:23.7668424Z     <doctest:/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0>:1: 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 ../aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
2025-03-14T04:33:23.7670075Z     
2025-03-14T04:33:23.7670386Z --- Runtime Warning: 5 / 10 ---
2025-03-14T04:33:23.7670939Z example = <DocTest(torch._functorch.eager_transforms linearize:0 ln 1704) at 0x7f95fcb429c0>
2025-03-14T04:33:23.7672837Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:264: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
2025-03-14T04:33:23.7674597Z       new_node = root_const_gm.graph.get_attr(in_node.target)
2025-03-14T04:33:23.7674970Z     
2025-03-14T04:33:23.7675280Z --- Runtime Warning: 6 / 10 ---
2025-03-14T04:33:23.7675810Z example = <DocTest(torch.nn.modules.transformer Transformer:0 ln 88) at 0x7f95fcb42f10>
2025-03-14T04:33:23.7677295Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:382: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)
2025-03-14T04:33:23.7678563Z       warnings.warn(
2025-03-14T04:33:23.7678878Z     
2025-03-14T04:33:23.7679282Z --- Runtime Warning: 7 / 10 ---
2025-03-14T04:33:23.7679863Z example = <DocTest(torch.nn.modules.transformer TransformerEncoder:0 ln 326) at 0x7f95fca04270>
2025-03-14T04:33:23.7681388Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:382: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)
2025-03-14T04:33:23.7682667Z       warnings.warn(
2025-03-14T04:33:23.7682919Z     
2025-03-14T04:33:23.7683233Z --- Runtime Warning: 8 / 10 ---
2025-03-14T04:33:23.7683742Z example = <DocTest(torch.nn.utils.weight_norm weight_norm:0 ln 133) at 0x7f95fca06250>
2025-03-14T04:33:23.7684942Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
2025-03-14T04:33:23.7685995Z       WeightNorm.apply(module, name, dim)
2025-03-14T04:33:23.7686305Z     
2025-03-14T04:33:23.7686647Z --- Runtime Warning: 9 / 10 ---
2025-03-14T04:33:23.7687194Z example = <DocTest(torch.nn.utils.weight_norm remove_weight_norm:0 ln 155) at 0x7f95fca06360>
2025-03-14T04:33:23.7688415Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
2025-03-14T04:33:23.7689451Z       WeightNorm.apply(module, name, dim)
2025-03-14T04:33:23.7689775Z     
2025-03-14T04:33:23.7690086Z --- Runtime Warning: 10 / 10 ---
2025-03-14T04:33:23.7690608Z example = <DocTest(torch.utils._pytree register_dataclass:0 ln 268) at 0x7f95fca74e20>
2025-03-14T04:33:23.7691836Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py:453: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
2025-03-14T04:33:23.7692893Z       _register_pytree_node(
2025-03-14T04:33:23.7693179Z     
2025-03-14T04:33:23.7693611Z === 342 passed, 367 skipped, 126 warnings in 11.79 seconds ===
2025-03-14T04:33:23.7694145Z Running test_autoload_enable 1/1 ... [2025-03-14 04:33:23.548896]
2025-03-14T04:33:26.1784971Z running install
2025-03-14T04:33:26.1786792Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:33:26.1787625Z !!
2025-03-14T04:33:26.1787769Z 
2025-03-14T04:33:26.1787906Z         ********************************************************************************
2025-03-14T04:33:26.1788324Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:33:26.1788755Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:33:26.1789152Z         standards-based tools.
2025-03-14T04:33:26.1789361Z 
2025-03-14T04:33:26.1789687Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:33:26.1790253Z         ********************************************************************************
2025-03-14T04:33:26.1790515Z 
2025-03-14T04:33:26.1790626Z !!
2025-03-14T04:33:26.1790873Z   self.initialize_options()
2025-03-14T04:33:26.1916578Z running build
2025-03-14T04:33:26.1917088Z running build_py
2025-03-14T04:33:26.1994553Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:33:26.1996624Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:33:26.1999566Z running build_ext
2025-03-14T04:33:26.2784543Z building 'torch_test_cpp_extension.cpp' extension
2025-03-14T04:33:26.2785745Z creating build/temp.linux-x86_64-cpython-313
2025-03-14T04:33:26.2790796Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/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
2025-03-14T04:33:27.4205137Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2025-03-14T04:33:27.4206190Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2025-03-14T04:33:27.4207091Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9,
2025-03-14T04:33:27.4207671Z                  from extension.cpp:1:
2025-03-14T04:33:27.4209145Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2025-03-14T04:33:27.4210014Z extension.cpp:45:53:   required from here
2025-03-14T04:33:27.4211499Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2025-03-14T04:33:27.4212848Z  1539 | class class_ : public detail::generic_type {
2025-03-14T04:33:27.4213218Z       |       ^~~~~~
2025-03-14T04:33:27.4215119Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2025-03-14T04:33:27.4216652Z extension.cpp:45:53:   required from here
2025-03-14T04:33:27.4219995Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2025-03-14T04:33:27.4222678Z  1599 |             with_internals([&](internals &internals) {
2025-03-14T04:33:27.4223088Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:27.4223644Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2025-03-14T04:33:27.4224247Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:27.4224724Z  1601 |                                                       : internals.registered_types_cpp;
2025-03-14T04:33:27.4225166Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:27.4225612Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2025-03-14T04:33:27.4226044Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:27.4226463Z  1603 |                     = instances[std::type_index(typeid(type))];
2025-03-14T04:33:27.4226881Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:27.4227231Z  1604 |             });
2025-03-14T04:33:27.4227566Z       |             ~               
2025-03-14T04:33:27.4230787Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:27.8406439Z building 'torch_test_cpp_extension.maia' extension
2025-03-14T04:33:27.8410966Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:33:28.8554397Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:29.2509861Z building 'torch_test_cpp_extension.rng' extension
2025-03-14T04:33:29.2514493Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/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
2025-03-14T04:33:30.4129920Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:30.4130887Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:30.4131891Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:30.4132822Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:30.4133518Z                  from rng_extension.cpp:6:
2025-03-14T04:33:30.4134515Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1158: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:30.4135420Z  1158 | # pragma unroll
2025-03-14T04:33:30.4135695Z       | 
2025-03-14T04:33:30.4136258Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1198,
2025-03-14T04:33:30.4137168Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:30.4137992Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:30.4138789Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:30.4139704Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:30.4140461Z                  from rng_extension.cpp:6:
2025-03-14T04:33:30.4141686Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:30.4142458Z    59 | #pragma unroll
2025-03-14T04:33:30.4142790Z       | 
2025-03-14T04:33:30.4143492Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:30.4144266Z    72 | #pragma unroll
2025-03-14T04:33:30.4144533Z       | 
2025-03-14T04:33:30.4145219Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:30.4145994Z    87 | #pragma unroll
2025-03-14T04:33:30.4146254Z       | 
2025-03-14T04:33:30.4146817Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1199,
2025-03-14T04:33:30.4147727Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:30.4148554Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:30.4149560Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:30.4150483Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:30.4151238Z                  from rng_extension.cpp:6:
2025-03-14T04:33:30.4152059Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:30.4152865Z   153 | #pragma unroll
2025-03-14T04:33:30.4153137Z       | 
2025-03-14T04:33:30.4156248Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:30.8321225Z running install_lib
2025-03-14T04:33:30.8406923Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:30.8514140Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:30.8619575Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:30.8734452Z running install_egg_info
2025-03-14T04:33:30.8910204Z running egg_info
2025-03-14T04:33:30.8978838Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-14T04:33:30.8982012Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-14T04:33:30.8983741Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-14T04:33:30.8985619Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-14T04:33:30.9059935Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:33:30.9068082Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:33:30.9069478Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-14T04:33:30.9071121Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-14T04:33:30.9076724Z running install_scripts
2025-03-14T04:33:34.2595393Z 
2025-03-14T04:33:34.2595854Z Running tests...
2025-03-14T04:33:34.2596257Z ----------------------------------------------------------------------
2025-03-14T04:33:34.2706930Z .
2025-03-14T04:33:34.2707252Z ----------------------------------------------------------------------
2025-03-14T04:33:34.2707644Z Ran 1 test in 0.011s
2025-03-14T04:33:34.2707824Z 
2025-03-14T04:33:34.2707914Z OK
2025-03-14T04:33:34.2708046Z 
2025-03-14T04:33:34.2708163Z Generating XML reports...
2025-03-14T04:33:34.9610253Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-03-14 04:33:34.960717]
2025-03-14T04:33:37.6058094Z running install
2025-03-14T04:33:37.6059493Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:33:37.6060306Z !!
2025-03-14T04:33:37.6060443Z 
2025-03-14T04:33:37.6060581Z         ********************************************************************************
2025-03-14T04:33:37.6061009Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:33:37.6061428Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:33:37.6062009Z         standards-based tools.
2025-03-14T04:33:37.6062225Z 
2025-03-14T04:33:37.6062537Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:33:37.6063090Z         ********************************************************************************
2025-03-14T04:33:37.6063354Z 
2025-03-14T04:33:37.6063443Z !!
2025-03-14T04:33:37.6063685Z   self.initialize_options()
2025-03-14T04:33:37.6191403Z running build
2025-03-14T04:33:37.6191676Z running build_py
2025-03-14T04:33:37.6268943Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:33:37.6270871Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-14T04:33:37.6274394Z running build_ext
2025-03-14T04:33:37.7055057Z building 'torch_test_cpp_extension.cpp' extension
2025-03-14T04:33:37.7055736Z creating build/temp.linux-x86_64-cpython-313
2025-03-14T04:33:37.7060774Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/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
2025-03-14T04:33:38.7602069Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2025-03-14T04:33:38.7603087Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2025-03-14T04:33:38.7603987Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9,
2025-03-14T04:33:38.7604550Z                  from extension.cpp:1:
2025-03-14T04:33:38.7605853Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2025-03-14T04:33:38.7606711Z extension.cpp:45:53:   required from here
2025-03-14T04:33:38.7608187Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2025-03-14T04:33:38.7609660Z  1539 | class class_ : public detail::generic_type {
2025-03-14T04:33:38.7610027Z       |       ^~~~~~
2025-03-14T04:33:38.7611637Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2025-03-14T04:33:38.7613024Z extension.cpp:45:53:   required from here
2025-03-14T04:33:38.7616234Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2025-03-14T04:33:38.7618934Z  1599 |             with_internals([&](internals &internals) {
2025-03-14T04:33:38.7619405Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:38.7619946Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2025-03-14T04:33:38.7620538Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:38.7621012Z  1601 |                                                       : internals.registered_types_cpp;
2025-03-14T04:33:38.7621434Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:38.7621876Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2025-03-14T04:33:38.7622305Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:38.7622719Z  1603 |                     = instances[std::type_index(typeid(type))];
2025-03-14T04:33:38.7623131Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-14T04:33:38.7623480Z  1604 |             });
2025-03-14T04:33:38.7623750Z       |             ~               
2025-03-14T04:33:38.7626976Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:39.1810146Z building 'torch_test_cpp_extension.maia' extension
2025-03-14T04:33:39.1814644Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-14T04:33:40.1507949Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:40.5489170Z building 'torch_test_cpp_extension.rng' extension
2025-03-14T04:33:40.5493702Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/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
2025-03-14T04:33:41.7517997Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:41.7518973Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:41.7519900Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:41.7520830Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:41.7521540Z                  from rng_extension.cpp:6:
2025-03-14T04:33:41.7522372Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1158: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:41.7523182Z  1158 | # pragma unroll
2025-03-14T04:33:41.7523437Z       | 
2025-03-14T04:33:41.7523994Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1198,
2025-03-14T04:33:41.7524910Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:41.7525916Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:41.7527028Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:41.7527962Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:41.7528650Z                  from rng_extension.cpp:6:
2025-03-14T04:33:41.7529450Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:41.7530236Z    59 | #pragma unroll
2025-03-14T04:33:41.7530504Z       | 
2025-03-14T04:33:41.7531193Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:41.7531968Z    72 | #pragma unroll
2025-03-14T04:33:41.7532231Z       | 
2025-03-14T04:33:41.7532915Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:41.7533772Z    87 | #pragma unroll
2025-03-14T04:33:41.7534035Z       | 
2025-03-14T04:33:41.7534587Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1199,
2025-03-14T04:33:41.7535502Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-14T04:33:41.7536325Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-14T04:33:41.7537127Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-14T04:33:41.7538047Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-14T04:33:41.7538736Z                  from rng_extension.cpp:6:
2025-03-14T04:33:41.7539550Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-14T04:33:41.7540347Z   153 | #pragma unroll
2025-03-14T04:33:41.7540611Z       | 
2025-03-14T04:33:41.7543763Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-14T04:33:42.1902539Z running install_lib
2025-03-14T04:33:42.1982226Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:42.2089264Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:42.2195269Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-14T04:33:42.2308069Z running install_egg_info
2025-03-14T04:33:42.2482048Z running egg_info
2025-03-14T04:33:42.2550339Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-14T04:33:42.2553894Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-14T04:33:42.2555576Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-14T04:33:42.2557584Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-14T04:33:42.2630401Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:33:42.2638620Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-14T04:33:42.2640262Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-14T04:33:42.2642028Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-14T04:33:42.2647877Z running install_scripts
2025-03-14T04:33:44.4860880Z running install
2025-03-14T04:33:44.4861981Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-14T04:33:44.4862809Z !!
2025-03-14T04:33:44.4862932Z 
2025-03-14T04:33:44.4863309Z         ********************************************************************************
2025-03-14T04:33:44.4863726Z         Please avoid running ``setup.py`` directly.
2025-03-14T04:33:44.4864155Z         Instead, use pypa/build, pypa/installer or other
2025-03-14T04:33:44.4864533Z         standards-based tools.
2025-03-14T04:33:44.4864746Z 
2025-03-14T04:33:44.4865071Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-14T04:33:44.4865618Z         ********************************************************************************
2025-03-14T04:33:44.4865880Z 
2025-03-14T04:33:44.4865968Z !!
2025-03-14T04:33:44.4866209Z   self.initialize_options()
2025-03-14T04:33:44.4999003Z running build
2025-03-14T04:33:44.4999321Z running build_ext
2025-03-14T04:33:44.6074538Z building 'no_python_abi_suffix_test' extension
2025-03-14T04:33:44.6372335Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-14T04:33:44.6373219Z Compiling objects...
2025-03-14T04:33:44.6373571Z Using envvar MAX_JOBS (14) as the number of workers...
2025-03-14T04:33:44.6633153Z ninja: no work to do.
2025-03-14T04:33:44.6678500Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so
2025-03-14T04:33:44.7272526Z running install_lib
2025-03-14T04:33:44.7351386Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-14T04:33:44.7356969Z running install_egg_info
2025-03-14T04:33:44.7532214Z running egg_info
2025-03-14T04:33:44.7601965Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2025-03-14T04:33:44.7605765Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2025-03-14T04:33:44.7608605Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2025-03-14T04:33:44.7680934Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-14T04:33:44.7689095Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-14T04:33:44.7690663Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-14T04:33:44.7692692Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info
2025-03-14T04:33:44.7697915Z running install_scripts
2025-03-14T04:33:45.2327797Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:33:45.2330607Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:33:45.232840]
2025-03-14T04:33:48.6890519Z 
2025-03-14T04:33:48.6891576Z test_cpp_extensions_aot_no_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_3a40611d50035192_.log
2025-03-14T04:33:48.6899526Z Running 20 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_libtorch_agnostic, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library
2025-03-14T04:33:48.6906642Z 
2025-03-14T04:33:48.6906880Z Running dynamo/test_cudagraphs 1/1 ... [2025-03-14 04:33:48.689462]
2025-03-14T04:33:48.6907337Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:33:48.6908457Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:33:48.689787]
2025-03-14T04:33:51.9496355Z 
2025-03-14T04:33:51.9497769Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_c4a6ff428a9f0791_.log
2025-03-14T04:33:51.9498973Z 
2025-03-14T04:33:51.9500724Z Running inductor/test_cudacodecache 1/1 ... [2025-03-14 04:33:51.949890]
2025-03-14T04:33:51.9501449Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:33:51.9506157Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cudacodecache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:33:51.950295]
2025-03-14T04:33:55.2532831Z 
2025-03-14T04:33:55.2534036Z inductor/test_cudacodecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudacodecache_1.1_8065e308ca916491_.log
2025-03-14T04:33:55.2534885Z 
2025-03-14T04:33:55.2536531Z Running inductor/test_torchinductor_strided_blocks 1/1 ... [2025-03-14 04:33:55.253484]
2025-03-14T04:33:55.2537073Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:33:55.2540041Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_strided_blocks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:33:55.253804]
2025-03-14T04:34:02.2222850Z 
2025-03-14T04:34:02.2224589Z inductor/test_torchinductor_strided_blocks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_strided_blocks_1.1_c0821735754532ea_.log
2025-03-14T04:34:02.2225993Z 
2025-03-14T04:34:02.2227177Z Running inductor/test_triton_cpu_backend 1/1 ... [2025-03-14 04:34:02.222538]
2025-03-14T04:34:02.2227937Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:02.2232684Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_triton_cpu_backend.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:02.222958]
2025-03-14T04:34:09.1716185Z 
2025-03-14T04:34:09.1717252Z inductor/test_triton_cpu_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_cpu_backend_1.1_c5e054ea4e91fe8f_.log
2025-03-14T04:34:09.1718030Z 
2025-03-14T04:34:09.1719496Z Running export/test_export_legacy 1/1 ... [2025-03-14 04:34:09.171787]
2025-03-14T04:34:09.1719960Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:09.1722758Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_export_legacy.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:09.172081]
2025-03-14T04:34:16.0338582Z 
2025-03-14T04:34:16.0340076Z export/test_export_legacy 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_export_legacy_1.1_109976f8265b5166_.log
2025-03-14T04:34:16.0341271Z 
2025-03-14T04:34:16.0343442Z Running inductor/test_aot_inductor_custom_ops 1/1 ... [2025-03-14 04:34:16.034125]
2025-03-14T04:34:16.0344285Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:16.0348601Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_aot_inductor_custom_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:16.034537]
2025-03-14T04:34:22.9870971Z 
2025-03-14T04:34:22.9872079Z inductor/test_aot_inductor_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_aot_inductor_custom_ops_1.1_b3f936f25fa184e1_.log
2025-03-14T04:34:22.9872934Z 
2025-03-14T04:34:22.9874503Z Running inductor/test_torchinductor_dynamic_shapes 4/4 ... [2025-03-14 04:34:22.987266]
2025-03-14T04:34:22.9875051Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:22.9878323Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_dynamic_shapes.py', '-m', 'serial', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:22.987605]
2025-03-14T04:34:30.3615495Z 
2025-03-14T04:34:30.3616627Z inductor/test_torchinductor_dynamic_shapes 4/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_dynamic_shapes_4.4_6022bf8175e4ace5_.log
2025-03-14T04:34:30.3617637Z Running 0 items in this shard:
2025-03-14T04:34:30.3618217Z 
2025-03-14T04:34:30.3618728Z Running inductor/test_op_dtype_prop 1/1 ... [2025-03-14 04:34:30.361713]
2025-03-14T04:34:30.3619217Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:30.3622164Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_op_dtype_prop.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:30.362025]
2025-03-14T04:34:38.0716832Z 
2025-03-14T04:34:38.0717899Z inductor/test_op_dtype_prop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_op_dtype_prop_1.1_a8cf456b7f25e110_.log
2025-03-14T04:34:38.0718709Z 
2025-03-14T04:34:38.0719915Z Running inductor/test_pattern_matcher 1/1 ... [2025-03-14 04:34:38.071838]
2025-03-14T04:34:38.0720471Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:38.0723591Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_pattern_matcher.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:38.072158]
2025-03-14T04:34:44.7386106Z 
2025-03-14T04:34:44.7387118Z inductor/test_pattern_matcher 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_pattern_matcher_1.1_09ef4a510940c119_.log
2025-03-14T04:34:44.7387918Z 
2025-03-14T04:34:44.7389389Z Running inductor/test_torchinductor_codegen_dynamic_shapes 2/4 ... [2025-03-14 04:34:44.738779]
2025-03-14T04:34:44.7389969Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:44.7393026Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'serial', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:44.739100]
2025-03-14T04:34:52.2632125Z 
2025-03-14T04:34:52.2633348Z inductor/test_torchinductor_codegen_dynamic_shapes 2/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.4_01403b3c6ec4eeab_.log
2025-03-14T04:34:52.2634483Z Running 0 items in this shard:
2025-03-14T04:34:52.2634684Z 
2025-03-14T04:34:52.2636031Z Running inductor/test_torchinductor_codegen_dynamic_shapes 3/4 ... [2025-03-14 04:34:52.263411]
2025-03-14T04:34:52.2636608Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:52.2639143Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:52.263712]
2025-03-14T04:34:59.7377744Z 
2025-03-14T04:34:59.7379523Z inductor/test_torchinductor_codegen_dynamic_shapes 3/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_3.4_41b70ff0f379c028_.log
2025-03-14T04:34:59.7381244Z Running 0 items in this shard:
2025-03-14T04:34:59.7381564Z 
2025-03-14T04:34:59.7382079Z Running inductor/test_torchinductor_codegen_dynamic_shapes 4/4 ... [2025-03-14 04:34:59.737926]
2025-03-14T04:34:59.7383019Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:34:59.7384764Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'serial', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:34:59.738222]
2025-03-14T04:35:27.2389975Z 
2025-03-14T04:35:27.2391452Z inductor/test_torchinductor_codegen_dynamic_shapes 4/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_4.4_0accef960df93b77_.log
2025-03-14T04:35:27.2393146Z Running 1 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_block_sizes_dynamic_shapes_cpu
2025-03-14T04:35:27.2393915Z 
2025-03-14T04:35:27.2394187Z Running inductor/test_cpu_select_algorithm 5/16 ... [2025-03-14 04:35:27.239136]
2025-03-14T04:35:27.2394688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:35:27.2396325Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=5', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:35:27.239425]
2025-03-14T04:35:34.7636213Z 
2025-03-14T04:35:34.7637338Z inductor/test_cpu_select_algorithm 5/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_5.16_eb47c0ef8e6c9e7a_.log
2025-03-14T04:35:34.7638389Z Running 0 items in this shard:
2025-03-14T04:35:34.7638605Z 
2025-03-14T04:35:34.7639982Z Running inductor/test_cpu_select_algorithm 13/16 ... [2025-03-14 04:35:34.763818]
2025-03-14T04:35:34.7640745Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:35:34.7644011Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=13', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:35:34.764162]
2025-03-14T04:35:42.2882823Z 
2025-03-14T04:35:42.2883878Z inductor/test_cpu_select_algorithm 13/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_13.16_3a54585922a368f5_.log
2025-03-14T04:35:42.2884828Z Running 0 items in this shard:
2025-03-14T04:35:42.2885044Z 
2025-03-14T04:35:42.2886275Z Running inductor/test_cpu_select_algorithm 15/16 ... [2025-03-14 04:35:42.288477]
2025-03-14T04:35:42.2886790Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:35:42.2889959Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=15', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:35:42.288790]
2025-03-14T04:35:49.7629283Z 
2025-03-14T04:35:49.7630552Z inductor/test_cpu_select_algorithm 15/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_15.16_d9b6b2fedbc757bf_.log
2025-03-14T04:35:49.7631503Z Running 0 items in this shard:
2025-03-14T04:35:49.7631701Z 
2025-03-14T04:35:49.7633015Z Running inductor/test_torchinductor_opinfo 3/42 ... [2025-03-14 04:35:49.763131]
2025-03-14T04:35:49.7633530Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:35:49.7636313Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=3', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:35:49.763432]
2025-03-14T04:35:58.5892013Z 
2025-03-14T04:35:58.5893050Z inductor/test_torchinductor_opinfo 3/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_3.42_d079b1d31fd0c45b_.log
2025-03-14T04:35:58.5894015Z Running 0 items in this shard:
2025-03-14T04:35:58.5894337Z 
2025-03-14T04:35:58.5895307Z Running inductor/test_torchinductor_opinfo 10/42 ... [2025-03-14 04:35:58.589368]
2025-03-14T04:35:58.5895824Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:35:58.5898837Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=10', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:35:58.589665]
2025-03-14T04:36:07.4154305Z 
2025-03-14T04:36:07.4155407Z inductor/test_torchinductor_opinfo 10/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_10.42_5b24981e5b93cb9e_.log
2025-03-14T04:36:07.4156444Z Running 0 items in this shard:
2025-03-14T04:36:07.4156694Z 
2025-03-14T04:36:07.4157393Z Running inductor/test_torchinductor_opinfo 13/42 ... [2025-03-14 04:36:07.415585]
2025-03-14T04:36:07.4157949Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:36:07.4160714Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=13', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:36:07.415862]
2025-03-14T04:36:16.2415725Z 
2025-03-14T04:36:16.2416849Z inductor/test_torchinductor_opinfo 13/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_13.42_6fd9074ad2db99be_.log
2025-03-14T04:36:16.2417821Z Running 0 items in this shard:
2025-03-14T04:36:16.2418271Z 
2025-03-14T04:36:16.2419391Z Running inductor/test_torchinductor_opinfo 18/42 ... [2025-03-14 04:36:16.241780]
2025-03-14T04:36:16.2419912Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:36:16.2423268Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=18', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:36:16.242109]
2025-03-14T04:36:25.1183498Z 
2025-03-14T04:36:25.1184598Z inductor/test_torchinductor_opinfo 18/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_18.42_a218ad608c679b00_.log
2025-03-14T04:36:25.1185552Z Running 0 items in this shard:
2025-03-14T04:36:25.1185788Z 
2025-03-14T04:36:25.1186533Z Running inductor/test_torchinductor_opinfo 25/42 ... [2025-03-14 04:36:25.118499]
2025-03-14T04:36:25.1187053Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:36:25.1190669Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=25', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:36:25.118813]
2025-03-14T04:36:34.0452360Z 
2025-03-14T04:36:34.0453420Z inductor/test_torchinductor_opinfo 25/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_25.42_a457a69438ca740a_.log
2025-03-14T04:36:34.0454401Z Running 0 items in this shard:
2025-03-14T04:36:34.0454736Z 
2025-03-14T04:36:34.0455982Z Running inductor/test_torchinductor_opinfo 28/42 ... [2025-03-14 04:36:34.045408]
2025-03-14T04:36:34.0456542Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:36:34.0459148Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=28', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:36:34.045706]
2025-03-14T04:36:42.8723619Z 
2025-03-14T04:36:42.8724958Z inductor/test_torchinductor_opinfo 28/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_28.42_216c96bb43d6e150_.log
2025-03-14T04:36:42.8725992Z Running 0 items in this shard:
2025-03-14T04:36:42.8726265Z 
2025-03-14T04:36:42.8726866Z Running inductor/test_torchinductor_opinfo 33/42 ... [2025-03-14 04:36:42.872528]
2025-03-14T04:36:42.8727447Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:36:42.8730641Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=33', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:36:42.872830]
2025-03-14T04:36:51.7488725Z 
2025-03-14T04:36:51.7490495Z inductor/test_torchinductor_opinfo 33/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_33.42_0d14952c9a35b034_.log
2025-03-14T04:36:51.7492285Z Running 0 items in this shard:
2025-03-14T04:36:51.7492687Z 
2025-03-14T04:36:51.7493238Z Running inductor/test_torchinductor_opinfo 40/42 ... [2025-03-14 04:36:51.749003]
2025-03-14T04:36:51.7494202Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:36:51.7496455Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=40', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:36:51.749288]
2025-03-14T04:37:00.5249388Z 
2025-03-14T04:37:00.5250500Z inductor/test_torchinductor_opinfo 40/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_40.42_674e49edc0e1f28b_.log
2025-03-14T04:37:00.5251707Z Running 0 items in this shard:
2025-03-14T04:37:00.5251950Z 
2025-03-14T04:37:00.5252638Z Running inductor/test_block_analysis 1/1 ... [2025-03-14 04:37:00.525084]
2025-03-14T04:37:00.5253112Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:00.5256104Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_block_analysis.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:00.525387]
2025-03-14T04:37:07.3986717Z 
2025-03-14T04:37:07.3987999Z inductor/test_block_analysis 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_block_analysis_1.1_0d67e63cfdb2852a_.log
2025-03-14T04:37:07.3988886Z Running 0 items in this shard:
2025-03-14T04:37:07.3989085Z 
2025-03-14T04:37:07.3989837Z Running dynamo/test_sources 1/1 ... [2025-03-14 04:37:07.398840]
2025-03-14T04:37:07.3990478Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:07.3993973Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:07.399151]
2025-03-14T04:37:10.7677474Z 
2025-03-14T04:37:10.7678662Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_f1d2e1ed24c58825_.log
2025-03-14T04:37:10.7679524Z Running 0 items in this shard:
2025-03-14T04:37:10.7679744Z 
2025-03-14T04:37:10.7681319Z Running inductor/test_fuzzer 1/1 ... [2025-03-14 04:37:10.767961]
2025-03-14T04:37:10.7681999Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:10.7684872Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fuzzer.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:10.768260]
2025-03-14T04:37:17.5914718Z 
2025-03-14T04:37:17.5915725Z inductor/test_fuzzer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fuzzer_1.1_4d3af2cbbe2db630_.log
2025-03-14T04:37:17.5916547Z Running 0 items in this shard:
2025-03-14T04:37:17.5916748Z 
2025-03-14T04:37:17.5917856Z Running inductor/test_custom_lowering 1/1 ... [2025-03-14 04:37:17.591637]
2025-03-14T04:37:17.5918392Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:17.5921955Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_custom_lowering.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:17.591939]
2025-03-14T04:37:24.4153435Z 
2025-03-14T04:37:24.4154492Z inductor/test_custom_lowering 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_custom_lowering_1.1_325afb0f89916fe5_.log
2025-03-14T04:37:24.4155391Z Running 0 items in this shard:
2025-03-14T04:37:24.4155718Z 
2025-03-14T04:37:24.4156334Z Running dynamo/test_recompiles 1/1 ... [2025-03-14 04:37:24.415490]
2025-03-14T04:37:24.4156839Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:24.4160144Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:24.415785]
2025-03-14T04:37:27.8845720Z 
2025-03-14T04:37:27.8846695Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_e5925c8ab6104485_.log
2025-03-14T04:37:27.8847779Z Running 0 items in this shard:
2025-03-14T04:37:27.8848025Z 
2025-03-14T04:37:27.8849190Z Running test_namedtensor 1/1 ... [2025-03-14 04:37:27.884756]
2025-03-14T04:37:27.8849620Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:27.8852577Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtensor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:27.885060]
2025-03-14T04:37:31.3538550Z 
2025-03-14T04:37:31.3539453Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_1240935933a14ac2_.log
2025-03-14T04:37:31.3540274Z Running 0 items in this shard:
2025-03-14T04:37:31.3540555Z 
2025-03-14T04:37:31.3541816Z Running dynamo/test_optimizers 1/1 ... [2025-03-14 04:37:31.354030]
2025-03-14T04:37:31.3542346Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:31.3545405Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:31.354338]
2025-03-14T04:37:34.7230001Z 
2025-03-14T04:37:34.7231588Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_8d14bb74e35a7be6_.log
2025-03-14T04:37:34.7232511Z Running 0 items in this shard:
2025-03-14T04:37:34.7232712Z 
2025-03-14T04:37:34.7233263Z Running inductor/test_cuda_repro 1/1 ... [2025-03-14 04:37:34.723184]
2025-03-14T04:37:34.7233777Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:34.7236852Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:34.723485]
2025-03-14T04:37:41.4897978Z 
2025-03-14T04:37:41.4898969Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_a2f27b83f0ebc5bc_.log
2025-03-14T04:37:41.4899688Z 
2025-03-14T04:37:41.4901141Z Running inductor/test_minifier 1/1 ... [2025-03-14 04:37:41.489960]
2025-03-14T04:37:41.4901663Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:41.4904557Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:41.490263]
2025-03-14T04:37:48.3135894Z 
2025-03-14T04:37:48.3137126Z inductor/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_1.1_f301f8f22a441333_.log
2025-03-14T04:37:48.3138100Z Running 0 items in this shard:
2025-03-14T04:37:48.3138302Z 
2025-03-14T04:37:48.3139168Z Running dynamo/test_modes 1/1 ... [2025-03-14 04:37:48.313772]
2025-03-14T04:37:48.3139659Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:48.3142865Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:48.314081]
2025-03-14T04:37:55.1373695Z 
2025-03-14T04:37:55.1374608Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_86d7c1eba22e479d_.log
2025-03-14T04:37:55.1375378Z Running 0 items in this shard:
2025-03-14T04:37:55.1375579Z 
2025-03-14T04:37:55.1376796Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2025-03-14 04:37:55.137525]
2025-03-14T04:37:55.1377404Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:55.1380208Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:55.137807]
2025-03-14T04:37:58.5565995Z 
2025-03-14T04:37:58.5567098Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_b1456cbc2ec1723e_.log
2025-03-14T04:37:58.5568107Z Running 0 items in this shard:
2025-03-14T04:37:58.5568323Z 
2025-03-14T04:37:58.5570146Z Running dynamo/test_decorators 1/1 ... [2025-03-14 04:37:58.556793]
2025-03-14T04:37:58.5570599Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:37:58.5573100Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_decorators.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:37:58.557103]
2025-03-14T04:38:01.9757668Z 
2025-03-14T04:38:01.9758855Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_5ac8024bbdd7efcb_.log
2025-03-14T04:38:01.9759761Z Running 0 items in this shard:
2025-03-14T04:38:01.9759963Z 
2025-03-14T04:38:01.9761258Z Running test_functionalization 1/1 ... [2025-03-14 04:38:01.975970]
2025-03-14T04:38:01.9761713Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:01.9764683Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_functionalization.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:01.976280]
2025-03-14T04:38:05.3950087Z 
2025-03-14T04:38:05.3951028Z test_functionalization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functionalization_1.1_d01d081b08d8c911_.log
2025-03-14T04:38:05.3951893Z Running 0 items in this shard:
2025-03-14T04:38:05.3952099Z 
2025-03-14T04:38:05.3953476Z Running inductor/test_triton_kernels 1/1 ... [2025-03-14 04:38:05.395189]
2025-03-14T04:38:05.3953965Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:05.3956928Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_triton_kernels.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:05.395502]
2025-03-14T04:38:12.2688045Z 
2025-03-14T04:38:12.2689276Z inductor/test_triton_kernels 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_kernels_1.1_e07915c44526d2ff_.log
2025-03-14T04:38:12.2690261Z Running 0 items in this shard:
2025-03-14T04:38:12.2690470Z 
2025-03-14T04:38:12.2691293Z Running nn/test_multihead_attention 1/1 ... [2025-03-14 04:38:12.268989]
2025-03-14T04:38:12.2691766Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:12.2694865Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_multihead_attention.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:12.269297]
2025-03-14T04:38:15.5378114Z 
2025-03-14T04:38:15.5379075Z nn/test_multihead_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_multihead_attention_1.1_f39f6079a89e233f_.log
2025-03-14T04:38:15.5379939Z Running 0 items in this shard:
2025-03-14T04:38:15.5380156Z 
2025-03-14T04:38:15.5381486Z Running test_stateless 1/1 ... [2025-03-14 04:38:15.537997]
2025-03-14T04:38:15.5381917Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:15.5385420Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_stateless.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:15.538358]
2025-03-14T04:38:18.9571199Z 
2025-03-14T04:38:18.9572143Z test_stateless 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_stateless_1.1_da3756cd090ea3fc_.log
2025-03-14T04:38:18.9572897Z Running 0 items in this shard:
2025-03-14T04:38:18.9573097Z 
2025-03-14T04:38:18.9574451Z Running export/test_db 1/1 ... [2025-03-14 04:38:18.957298]
2025-03-14T04:38:18.9574925Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:18.9578106Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_db.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:18.957605]
2025-03-14T04:38:22.4764988Z 
2025-03-14T04:38:22.4765852Z export/test_db 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_db_1.1_868fc98f7d33fd52_.log
2025-03-14T04:38:22.4766610Z Running 0 items in this shard:
2025-03-14T04:38:22.4766827Z 
2025-03-14T04:38:22.4768574Z Running inductor/test_perf 1/1 ... [2025-03-14 04:38:22.476681]
2025-03-14T04:38:22.4769017Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:22.4771704Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_perf.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:22.476978]
2025-03-14T04:38:29.1999898Z 
2025-03-14T04:38:29.2001303Z inductor/test_perf 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_perf_1.1_3c278a5ebb20a508_.log
2025-03-14T04:38:29.2002514Z 
2025-03-14T04:38:29.2005140Z Running test_content_store 1/1 ... [2025-03-14 04:38:29.200288]
2025-03-14T04:38:29.2005610Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:29.2008364Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_content_store.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:29.200627]
2025-03-14T04:38:32.5193687Z 
2025-03-14T04:38:32.5194589Z test_content_store 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_content_store_1.1_4fb7a88bde7b8a46_.log
2025-03-14T04:38:32.5195373Z Running 0 items in this shard:
2025-03-14T04:38:32.5195573Z 
2025-03-14T04:38:32.5197139Z Running inductor/test_async_compile 1/1 ... [2025-03-14 04:38:32.519546]
2025-03-14T04:38:32.5197926Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:32.5200629Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_async_compile.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:32.519864]
2025-03-14T04:38:39.3430421Z 
2025-03-14T04:38:39.3431253Z inductor/test_async_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_async_compile_1.1_49ce87db2b55ad62_.log
2025-03-14T04:38:39.3432108Z Running 0 items in this shard:
2025-03-14T04:38:39.3432308Z 
2025-03-14T04:38:39.3434175Z Running dynamo/test_autograd_function 1/1 ... [2025-03-14 04:38:39.343235]
2025-03-14T04:38:39.3434664Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:39.3437584Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:39.343550]
2025-03-14T04:38:46.1669013Z 
2025-03-14T04:38:46.1670116Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_4c45fb8f153804f3_.log
2025-03-14T04:38:46.1671011Z Running 0 items in this shard:
2025-03-14T04:38:46.1671232Z 
2025-03-14T04:38:46.1672486Z Running test_expanded_weights 1/1 ... [2025-03-14 04:38:46.167100]
2025-03-14T04:38:46.1672938Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:46.1676273Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_expanded_weights.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:46.167430]
2025-03-14T04:38:50.6876440Z 
2025-03-14T04:38:50.6877434Z test_expanded_weights 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_expanded_weights_1.1_340e3ddf86f9a1ab_.log
2025-03-14T04:38:50.6878268Z Running 0 items in this shard:
2025-03-14T04:38:50.6878486Z 
2025-03-14T04:38:50.6880349Z Running inductor/test_custom_post_grad_passes 1/1 ... [2025-03-14 04:38:50.687870]
2025-03-14T04:38:50.6880872Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:50.6884073Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_custom_post_grad_passes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:50.688181]
2025-03-14T04:38:57.5615736Z 
2025-03-14T04:38:57.5616795Z inductor/test_custom_post_grad_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_custom_post_grad_passes_1.1_974872ef4c49ac1e_.log
2025-03-14T04:38:57.5617778Z Running 0 items in this shard:
2025-03-14T04:38:57.5617978Z 
2025-03-14T04:38:57.5619327Z Running torch_np/test_basic 1/1 ... [2025-03-14 04:38:57.561780]
2025-03-14T04:38:57.5619772Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:38:57.5622808Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_basic.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:38:57.562085]
2025-03-14T04:39:01.0308884Z 
2025-03-14T04:39:01.0310267Z torch_np/test_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_basic_1.1_cdd9cdd933073565_.log
2025-03-14T04:39:01.0311545Z Running 0 items in this shard:
2025-03-14T04:39:01.0311858Z 
2025-03-14T04:39:01.0312777Z Running inductor/test_control_flow 1/1 ... [2025-03-14 04:39:01.031087]
2025-03-14T04:39:01.0313530Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:01.0316516Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_control_flow.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:01.031425]
2025-03-14T04:39:07.8549404Z 
2025-03-14T04:39:07.8550453Z inductor/test_control_flow 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_control_flow_1.1_3c0fc8da5dd9ea27_.log
2025-03-14T04:39:07.8551314Z Running 0 items in this shard:
2025-03-14T04:39:07.8551515Z 
2025-03-14T04:39:07.8552718Z Running inductor/test_ordered_set 1/1 ... [2025-03-14 04:39:07.855112]
2025-03-14T04:39:07.8553187Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:07.8556338Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_ordered_set.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:07.855434]
2025-03-14T04:39:11.3242647Z 
2025-03-14T04:39:11.3243722Z inductor/test_ordered_set 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_ordered_set_1.1_33607916ee09bfc4_.log
2025-03-14T04:39:11.3244556Z Running 0 items in this shard:
2025-03-14T04:39:11.3244759Z 
2025-03-14T04:39:11.3246003Z Running nn/test_packed_sequence 1/1 ... [2025-03-14 04:39:11.324449]
2025-03-14T04:39:11.3246467Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:11.3249996Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_packed_sequence.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:11.324767]
2025-03-14T04:39:14.7936792Z 
2025-03-14T04:39:14.7937640Z nn/test_packed_sequence 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_packed_sequence_1.1_150a1f3f682f9d95_.log
2025-03-14T04:39:14.7938471Z Running 0 items in this shard:
2025-03-14T04:39:14.7938679Z 
2025-03-14T04:39:14.7940220Z Running dynamo/test_metrics_context 1/1 ... [2025-03-14 04:39:14.793880]
2025-03-14T04:39:14.7940694Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:14.7944063Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_metrics_context.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:14.794187]
2025-03-14T04:39:18.1627677Z 
2025-03-14T04:39:18.1628522Z dynamo/test_metrics_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_metrics_context_1.1_d07f44acb26f09a4_.log
2025-03-14T04:39:18.1629374Z Running 0 items in this shard:
2025-03-14T04:39:18.1629574Z 
2025-03-14T04:39:18.1631157Z Running dynamo/test_base_hop 1/1 ... [2025-03-14 04:39:18.162982]
2025-03-14T04:39:18.1631601Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:18.1635292Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_hop.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:18.163321]
2025-03-14T04:39:24.9364776Z 
2025-03-14T04:39:24.9365722Z dynamo/test_base_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_hop_1.1_2b276481680902fa_.log
2025-03-14T04:39:24.9366517Z Running 0 items in this shard:
2025-03-14T04:39:24.9366720Z 
2025-03-14T04:39:24.9368620Z Running test_custom_ops 1/1 ... [2025-03-14 04:39:24.936706]
2025-03-14T04:39:24.9369025Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:24.9372700Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_custom_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:24.937050]
2025-03-14T04:39:29.1068503Z 
2025-03-14T04:39:29.1069488Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_a53a85fd7263401a_.log
2025-03-14T04:39:29.1070239Z Running 0 items in this shard:
2025-03-14T04:39:29.1070441Z 
2025-03-14T04:39:29.1072228Z Running dynamo/test_modules 1/1 ... [2025-03-14 04:39:29.107059]
2025-03-14T04:39:29.1072655Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:29.1075842Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:29.107374]
2025-03-14T04:39:36.1309750Z 
2025-03-14T04:39:36.1310764Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_849ec3cf7ed95343_.log
2025-03-14T04:39:36.1311803Z Running 0 items in this shard:
2025-03-14T04:39:36.1312020Z 
2025-03-14T04:39:36.1313065Z Running inductor/test_fused_attention 1/1 ... [2025-03-14 04:39:36.131148]
2025-03-14T04:39:36.1313550Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:36.1316852Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fused_attention.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:36.131463]
2025-03-14T04:39:42.9047557Z 
2025-03-14T04:39:42.9049246Z inductor/test_fused_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fused_attention_1.1_38218a8290941a08_.log
2025-03-14T04:39:42.9050665Z Running 0 items in this shard:
2025-03-14T04:39:42.9051024Z 
2025-03-14T04:39:42.9052125Z Running inductor/test_layout_optim 1/1 ... [2025-03-14 04:39:42.905031]
2025-03-14T04:39:42.9052862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:42.9057938Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_layout_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:42.905452]
2025-03-14T04:39:49.5702492Z 
2025-03-14T04:39:49.5703436Z inductor/test_layout_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_layout_optim_1.1_ae4ffa274a8d238a_.log
2025-03-14T04:39:49.5704188Z 
2025-03-14T04:39:49.5705860Z Running inductor/test_inductor_freezing 1/1 ... [2025-03-14 04:39:49.570427]
2025-03-14T04:39:49.5706353Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:49.5709284Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_freezing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:49.570731]
2025-03-14T04:39:56.8444699Z 
2025-03-14T04:39:56.8445736Z inductor/test_inductor_freezing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_freezing_1.1_eb812a36babc27a7_.log
2025-03-14T04:39:56.8446671Z Running 0 items in this shard:
2025-03-14T04:39:56.8446872Z 
2025-03-14T04:39:56.8448049Z Running inductor/test_mmdecomp 1/1 ... [2025-03-14 04:39:56.844631]
2025-03-14T04:39:56.8448508Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:39:56.8452064Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_mmdecomp.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:39:56.844971]
2025-03-14T04:40:03.6683304Z 
2025-03-14T04:40:03.6684250Z inductor/test_mmdecomp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mmdecomp_1.1_100c0683f11a9781_.log
2025-03-14T04:40:03.6685124Z Running 0 items in this shard:
2025-03-14T04:40:03.6685330Z 
2025-03-14T04:40:03.6686844Z Running inductor/test_binary_folding 1/1 ... [2025-03-14 04:40:03.668507]
2025-03-14T04:40:03.6687333Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:03.6689954Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_binary_folding.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:03.668800]
2025-03-14T04:40:10.9426662Z 
2025-03-14T04:40:10.9427679Z inductor/test_binary_folding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_binary_folding_1.1_66171e0d562da485_.log
2025-03-14T04:40:10.9428583Z Running 0 items in this shard:
2025-03-14T04:40:10.9428808Z 
2025-03-14T04:40:10.9430206Z Running dynamo/test_dicts 1/1 ... [2025-03-14 04:40:10.942865]
2025-03-14T04:40:10.9430643Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:10.9433646Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_dicts.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:10.943170]
2025-03-14T04:40:14.3618541Z 
2025-03-14T04:40:14.3619420Z dynamo/test_dicts 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dicts_1.1_a5aee4150949867c_.log
2025-03-14T04:40:14.3620197Z Running 0 items in this shard:
2025-03-14T04:40:14.3620399Z 
2025-03-14T04:40:14.3621996Z Running dynamo/test_compiler_bisector 1/1 ... [2025-03-14 04:40:14.362047]
2025-03-14T04:40:14.3622516Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:14.3625446Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:14.362353]
2025-03-14T04:40:21.2357357Z 
2025-03-14T04:40:21.2358590Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_f136754a850d45c6_.log
2025-03-14T04:40:21.2359583Z Running 0 items in this shard:
2025-03-14T04:40:21.2359783Z 
2025-03-14T04:40:21.2360823Z Running test_legacy_vmap 1/1 ... [2025-03-14 04:40:21.235929]
2025-03-14T04:40:21.2361236Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:21.2364756Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:21.236254]
2025-03-14T04:40:24.5549464Z 
2025-03-14T04:40:24.5550341Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_5ac69a6058ae0851_.log
2025-03-14T04:40:24.5551108Z Running 0 items in this shard:
2025-03-14T04:40:24.5551325Z 
2025-03-14T04:40:24.5552690Z Running test_cuda_sanitizer 1/1 ... [2025-03-14 04:40:24.555097]
2025-03-14T04:40:24.5553114Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:24.5555944Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_sanitizer.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:24.555382]
2025-03-14T04:40:27.9188110Z 
2025-03-14T04:40:27.9189261Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_9652358fe8364749_.log
2025-03-14T04:40:27.9190137Z Running 0 items in this shard:
2025-03-14T04:40:27.9190346Z 
2025-03-14T04:40:27.9192103Z Running test_pytree 1/1 ... [2025-03-14 04:40:27.918986]
2025-03-14T04:40:27.9192546Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:27.9194585Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_pytree.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:27.919265]
2025-03-14T04:40:31.3378901Z 
2025-03-14T04:40:31.3379732Z test_pytree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_pytree_1.1_0e78ce93575d043a_.log
2025-03-14T04:40:31.3380450Z Running 0 items in this shard:
2025-03-14T04:40:31.3380665Z 
2025-03-14T04:40:31.3382029Z Running dynamo/test_flat_apply 1/1 ... [2025-03-14 04:40:31.338062]
2025-03-14T04:40:31.3382518Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:31.3385276Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:31.338343]
2025-03-14T04:40:34.7070272Z 
2025-03-14T04:40:34.7071239Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_250935a91aebc062_.log
2025-03-14T04:40:34.7072067Z Running 0 items in this shard:
2025-03-14T04:40:34.7072268Z 
2025-03-14T04:40:34.7073402Z Running export/test_passes 1/1 ... [2025-03-14 04:40:34.707205]
2025-03-14T04:40:34.7073842Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:34.7076724Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_passes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:34.707489]
2025-03-14T04:40:38.6769415Z 
2025-03-14T04:40:38.6770320Z export/test_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_passes_1.1_cb18ed034bc88b83_.log
2025-03-14T04:40:38.6771114Z Running 0 items in this shard:
2025-03-14T04:40:38.6771313Z 
2025-03-14T04:40:38.6772771Z Running dynamo/test_aot_autograd 1/1 ... [2025-03-14 04:40:38.677119]
2025-03-14T04:40:38.6773239Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:38.6776039Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:38.677405]
2025-03-14T04:40:42.0961958Z 
2025-03-14T04:40:42.0962951Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_4665e0072dd31b0c_.log
2025-03-14T04:40:42.0963808Z Running 0 items in this shard:
2025-03-14T04:40:42.0964039Z 
2025-03-14T04:40:42.0964909Z Running inductor/test_split_cat_fx_aten_passes 1/1 ... [2025-03-14 04:40:42.096360]
2025-03-14T04:40:42.0965421Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:42.0968438Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_split_cat_fx_aten_passes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:42.096652]
2025-03-14T04:40:48.8699370Z 
2025-03-14T04:40:48.8700438Z inductor/test_split_cat_fx_aten_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_split_cat_fx_aten_passes_1.1_75bae183b175afe9_.log
2025-03-14T04:40:48.8701621Z Running 0 items in this shard:
2025-03-14T04:40:48.8701913Z 
2025-03-14T04:40:48.8702342Z Running export/test_converter 1/1 ... [2025-03-14 04:40:48.870107]
2025-03-14T04:40:48.8702811Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:48.8706084Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_converter.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:48.870406]
2025-03-14T04:40:52.3392184Z 
2025-03-14T04:40:52.3393115Z export/test_converter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_converter_1.1_7674446b82306f87_.log
2025-03-14T04:40:52.3393931Z Running 0 items in this shard:
2025-03-14T04:40:52.3394135Z 
2025-03-14T04:40:52.3395852Z Running inductor/test_efficient_conv_bn_eval 1/1 ... [2025-03-14 04:40:52.339377]
2025-03-14T04:40:52.3396365Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:52.3398936Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_efficient_conv_bn_eval.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:52.339687]
2025-03-14T04:40:59.5633941Z 
2025-03-14T04:40:59.5634921Z inductor/test_efficient_conv_bn_eval 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_efficient_conv_bn_eval_1.1_c023d1ed2d9cc9ff_.log
2025-03-14T04:40:59.5635854Z Running 0 items in this shard:
2025-03-14T04:40:59.5636075Z 
2025-03-14T04:40:59.5637461Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2025-03-14 04:40:59.563582]
2025-03-14T04:40:59.5637984Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:40:59.5640920Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backward_higher_order_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:40:59.563883]
2025-03-14T04:41:02.9826829Z 
2025-03-14T04:41:02.9828410Z dynamo/test_backward_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backward_higher_order_ops_1.1_7364bab995a65aa0_.log
2025-03-14T04:41:02.9829986Z Running 0 items in this shard:
2025-03-14T04:41:02.9830305Z 
2025-03-14T04:41:02.9830900Z Running test_autograd 1/1 ... [2025-03-14 04:41:02.982827]
2025-03-14T04:41:02.9831542Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:02.9834163Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:02.983125]
2025-03-14T04:41:07.6535501Z 
2025-03-14T04:41:07.6536396Z test_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_1.1_8b5243876fe072c5_.log
2025-03-14T04:41:07.6537156Z Running 0 items in this shard:
2025-03-14T04:41:07.6537381Z 
2025-03-14T04:41:07.6538680Z Running test_functional_optim 1/1 ... [2025-03-14 04:41:07.653718]
2025-03-14T04:41:07.6539137Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:07.6542216Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_functional_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:07.654019]
2025-03-14T04:41:11.2230556Z 
2025-03-14T04:41:11.2231476Z test_functional_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_optim_1.1_240fe24c63e83303_.log
2025-03-14T04:41:11.2232277Z Running 0 items in this shard:
2025-03-14T04:41:11.2232497Z 
2025-03-14T04:41:11.2233883Z Running dynamo/test_resume 1/1 ... [2025-03-14 04:41:11.223231]
2025-03-14T04:41:11.2234323Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:11.2237332Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_resume.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:11.223541]
2025-03-14T04:41:14.5921513Z 
2025-03-14T04:41:14.5922468Z dynamo/test_resume 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_resume_1.1_3851bb177798a40c_.log
2025-03-14T04:41:14.5923253Z Running 0 items in this shard:
2025-03-14T04:41:14.5923470Z 
2025-03-14T04:41:14.5924883Z Running dynamo/test_export 1/1 ... [2025-03-14 04:41:14.592337]
2025-03-14T04:41:14.5925317Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:14.5928283Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:14.592637]
2025-03-14T04:41:18.0613362Z 
2025-03-14T04:41:18.0614389Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_7ba825f3438537f8_.log
2025-03-14T04:41:18.0615180Z Running 0 items in this shard:
2025-03-14T04:41:18.0615404Z 
2025-03-14T04:41:18.0616642Z Running dynamo/test_recompile_ux 1/1 ... [2025-03-14 04:41:18.061509]
2025-03-14T04:41:18.0617113Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:18.0619948Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:18.061805]
2025-03-14T04:41:21.4821632Z 
2025-03-14T04:41:21.4822543Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_385c8911b7dd4c3e_.log
2025-03-14T04:41:21.4823390Z Running 0 items in this shard:
2025-03-14T04:41:21.4823608Z 
2025-03-14T04:41:21.4824918Z Running dynamo/test_unspec 1/1 ... [2025-03-14 04:41:21.482351]
2025-03-14T04:41:21.4825355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:21.4828718Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:21.482667]
2025-03-14T04:41:24.9014738Z 
2025-03-14T04:41:24.9015657Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_fc184517bea09a7d_.log
2025-03-14T04:41:24.9017619Z Running 0 items in this shard:
2025-03-14T04:41:24.9017904Z 
2025-03-14T04:41:24.9019017Z Running nn/test_lazy_modules 1/1 ... [2025-03-14 04:41:24.901717]
2025-03-14T04:41:24.9019473Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:24.9022354Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_lazy_modules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:24.902036]
2025-03-14T04:41:28.3206413Z 
2025-03-14T04:41:28.3207500Z nn/test_lazy_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_lazy_modules_1.1_729a321c60e9b630_.log
2025-03-14T04:41:28.3208301Z Running 0 items in this shard:
2025-03-14T04:41:28.3208503Z 
2025-03-14T04:41:28.3210036Z Running inductor/test_codecache 1/1 ... [2025-03-14 04:41:28.320843]
2025-03-14T04:41:28.3210505Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:28.3213923Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_codecache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:28.321171]
2025-03-14T04:41:35.1943444Z 
2025-03-14T04:41:35.1944405Z inductor/test_codecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_codecache_1.1_e9fba67691abc45f_.log
2025-03-14T04:41:35.1945275Z Running 0 items in this shard:
2025-03-14T04:41:35.1945478Z 
2025-03-14T04:41:35.1947382Z Running test_numba_integration 1/1 ... [2025-03-14 04:41:35.194512]
2025-03-14T04:41:35.1947846Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:35.1950551Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_numba_integration.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:35.194836]
2025-03-14T04:41:38.6136103Z 
2025-03-14T04:41:38.6136997Z test_numba_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_numba_integration_1.1_16cdad20b7055190_.log
2025-03-14T04:41:38.6138065Z Running 0 items in this shard:
2025-03-14T04:41:38.6138279Z 
2025-03-14T04:41:38.6138983Z Running dynamo/test_logging 1/1 ... [2025-03-14 04:41:38.613772]
2025-03-14T04:41:38.6139421Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:38.6142655Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:38.614065]
2025-03-14T04:41:45.4373501Z 
2025-03-14T04:41:45.4374581Z dynamo/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_logging_1.1_0d4b7dc2845a0cbf_.log
2025-03-14T04:41:45.4375422Z Running 0 items in this shard:
2025-03-14T04:41:45.4375639Z 
2025-03-14T04:41:45.4377089Z Running test_decomp 1/9 ... [2025-03-14 04:41:45.437551]
2025-03-14T04:41:45.4377502Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:45.4380894Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=1', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:45.437873]
2025-03-14T04:41:51.3601332Z 
2025-03-14T04:41:51.3602182Z test_decomp 1/9 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_1.9_a9ccbfe08dc52876_.log
2025-03-14T04:41:51.3602903Z Running 0 items in this shard:
2025-03-14T04:41:51.3603122Z 
2025-03-14T04:41:51.3604649Z Running test_ops_gradients 2/2 ... [2025-03-14 04:41:51.360316]
2025-03-14T04:41:51.3605083Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:51.3608332Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_gradients.py', '-m', 'serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:51.360631]
2025-03-14T04:41:56.7820897Z 
2025-03-14T04:41:56.7821765Z test_ops_gradients 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_gradients_2.2_fd14b1fdf1e3d385_.log
2025-03-14T04:41:56.7822570Z Running 0 items in this shard:
2025-03-14T04:41:56.7822788Z 
2025-03-14T04:41:56.7824116Z Running test_type_hints 1/1 ... [2025-03-14 04:41:56.782288]
2025-03-14T04:41:56.7824542Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:41:56.7828001Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_hints.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:41:56.782583]
2025-03-14T04:42:00.2016128Z 
2025-03-14T04:42:00.2016995Z test_type_hints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_hints_1.1_059afb174ac40263_.log
2025-03-14T04:42:00.2017769Z Running 0 items in this shard:
2025-03-14T04:42:00.2017970Z 
2025-03-14T04:42:00.2020375Z Running lazy/test_extract_compiled_graph 1/1 ... [2025-03-14 04:42:00.201812]
2025-03-14T04:42:00.2020941Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:00.2023202Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:00.202115]
2025-03-14T04:42:01.6415236Z 
2025-03-14T04:42:01.6416262Z lazy/test_extract_compiled_graph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_extract_compiled_graph_1.1_08adad02f4de26fc_.log
2025-03-14T04:42:01.6417049Z 
2025-03-14T04:42:01.6418592Z Running test_meta 1/4 ... [2025-03-14 04:42:01.641706]
2025-03-14T04:42:01.6419268Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:01.6422154Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_meta.py', '-m', 'serial', '--shard-id=1', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:01.642021]
2025-03-14T04:42:12.7712781Z 
2025-03-14T04:42:12.7713610Z test_meta 1/4 was successful, full logs can be found in artifacts with path test/test-reports/test_meta_1.4_10b647f90a0d348a_.log
2025-03-14T04:42:12.7714309Z Running 0 items in this shard:
2025-03-14T04:42:12.7714507Z 
2025-03-14T04:42:12.7716051Z Running test_meta 2/4 ... [2025-03-14 04:42:12.771446]
2025-03-14T04:42:12.7716521Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:12.7719832Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_meta.py', '-m', 'serial', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:12.771759]
2025-03-14T04:42:23.8510576Z 
2025-03-14T04:42:23.8511414Z test_meta 2/4 was successful, full logs can be found in artifacts with path test/test-reports/test_meta_2.4_3d48ac560faddd68_.log
2025-03-14T04:42:23.8512121Z Running 0 items in this shard:
2025-03-14T04:42:23.8512320Z 
2025-03-14T04:42:23.8514038Z Running optim/test_optim 1/1 ... [2025-03-14 04:42:23.851220]
2025-03-14T04:42:23.8514485Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:23.8517035Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:23.851510]
2025-03-14T04:42:27.1744500Z 
2025-03-14T04:42:27.1745360Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_2f30caea7478742d_.log
2025-03-14T04:42:27.1746011Z 
2025-03-14T04:42:27.1747644Z Running test_ops 3/6 ... [2025-03-14 04:42:27.174615]
2025-03-14T04:42:27.1748115Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:27.1751325Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:27.174939]
2025-03-14T04:42:41.0581931Z 
2025-03-14T04:42:41.0582741Z test_ops 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_3.6_ff0f3a1068d3bcfc_.log
2025-03-14T04:42:41.0583437Z Running 0 items in this shard:
2025-03-14T04:42:41.0583654Z 
2025-03-14T04:42:41.0585217Z Running torch_np/numpy_tests/core/test_getlimits 1/1 ... [2025-03-14 04:42:41.058356]
2025-03-14T04:42:41.0586058Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:41.0589034Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_getlimits.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:41.058666]
2025-03-14T04:42:44.4772527Z 
2025-03-14T04:42:44.4773626Z torch_np/numpy_tests/core/test_getlimits 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_getlimits_1.1_db08f2407eeb37a6_.log
2025-03-14T04:42:44.4774605Z Running 0 items in this shard:
2025-03-14T04:42:44.4774898Z 
2025-03-14T04:42:44.4775788Z Running torch_np/numpy_tests/lib/test_shape_base_ 1/1 ... [2025-03-14 04:42:44.477432]
2025-03-14T04:42:44.4776312Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:44.4779472Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_shape_base_.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:44.477735]
2025-03-14T04:42:47.8964191Z 
2025-03-14T04:42:47.8965293Z torch_np/numpy_tests/lib/test_shape_base_ 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_shape_base__1.1_6da19678a4bac5fb_.log
2025-03-14T04:42:47.8966344Z Running 0 items in this shard:
2025-03-14T04:42:47.8966546Z 
2025-03-14T04:42:47.8967542Z Running test_view_ops 1/1 ... [2025-03-14 04:42:47.896589]
2025-03-14T04:42:47.8967957Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:47.8970934Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_view_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:47.896886]
2025-03-14T04:42:51.2655564Z 
2025-03-14T04:42:51.2656393Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_01ef216c204b268a_.log
2025-03-14T04:42:51.2657157Z Running 0 items in this shard:
2025-03-14T04:42:51.2657378Z 
2025-03-14T04:42:51.2659115Z Running test_jit_autocast 1/1 ... [2025-03-14 04:42:51.265761]
2025-03-14T04:42:51.2659544Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:51.2662711Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_autocast.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:51.266069]
2025-03-14T04:42:56.6872936Z 
2025-03-14T04:42:56.6873819Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_93db65f2b089d4e4_.log
2025-03-14T04:42:56.6874610Z Running 0 items in this shard:
2025-03-14T04:42:56.6874811Z 
2025-03-14T04:42:56.6876300Z Running nn/test_init 1/1 ... [2025-03-14 04:42:56.687471]
2025-03-14T04:42:56.6876725Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:42:56.6880073Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_init.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:42:56.687785]
2025-03-14T04:43:00.4570494Z 
2025-03-14T04:43:00.4571387Z nn/test_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_init_1.1_075df85dc4012057_.log
2025-03-14T04:43:00.4572114Z Running 0 items in this shard:
2025-03-14T04:43:00.4572317Z 
2025-03-14T04:43:00.4573588Z Running test_xnnpack_integration 1/1 ... [2025-03-14 04:43:00.457207]
2025-03-14T04:43:00.4574056Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:00.4577250Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_xnnpack_integration.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:00.457503]
2025-03-14T04:43:03.8762779Z 
2025-03-14T04:43:03.8763785Z test_xnnpack_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_xnnpack_integration_1.1_9d2c3ab3c9706713_.log
2025-03-14T04:43:03.8764822Z Running 0 items in this shard:
2025-03-14T04:43:03.8765042Z 
2025-03-14T04:43:03.8766302Z Running profiler/test_profiler_tree 1/1 ... [2025-03-14 04:43:03.876439]
2025-03-14T04:43:03.8766782Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:03.8769706Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_profiler_tree.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:03.876756]
2025-03-14T04:43:07.2453888Z 
2025-03-14T04:43:07.2454793Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_4f3bd7c3c7db90f3_.log
2025-03-14T04:43:07.2455887Z Running 0 items in this shard:
2025-03-14T04:43:07.2456136Z 
2025-03-14T04:43:07.2457200Z Running torch_np/test_ndarray_methods 1/1 ... [2025-03-14 04:43:07.245562]
2025-03-14T04:43:07.2457696Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:07.2460855Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_ndarray_methods.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:07.245872]
2025-03-14T04:43:10.7146860Z 
2025-03-14T04:43:10.7147872Z torch_np/test_ndarray_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ndarray_methods_1.1_26bae14d5917f031_.log
2025-03-14T04:43:10.7148895Z Running 0 items in this shard:
2025-03-14T04:43:10.7149131Z 
2025-03-14T04:43:10.7150374Z Running test_jit 1/1 ... [2025-03-14 04:43:10.714888]
2025-03-14T04:43:10.7150771Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:10.7154763Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:10.715198]
2025-03-14T04:43:15.9862846Z 
2025-03-14T04:43:15.9863646Z test_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_1.1_74ff0bd1a8db2cde_.log
2025-03-14T04:43:15.9864334Z Running 0 items in this shard:
2025-03-14T04:43:15.9864555Z 
2025-03-14T04:43:15.9866010Z Running test_serialization 1/1 ... [2025-03-14 04:43:15.986456]
2025-03-14T04:43:15.9866499Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:15.9869547Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_serialization.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:15.986758]
2025-03-14T04:43:40.6823775Z 
2025-03-14T04:43:40.6824679Z test_serialization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_serialization_1.1_e7bb15f3d917efd9_.log
2025-03-14T04:43:40.6826046Z Running 2 items in this shard: test/test_serialization.py::TestSerialization::test_serialization_2gb_file, test/test_serialization.py::TestSerialization::test_serialization_4gb_file
2025-03-14T04:43:40.6826999Z 
2025-03-14T04:43:40.6827292Z Running torch_np/numpy_tests/lib/test_twodim_base 1/1 ... [2025-03-14 04:43:40.682553]
2025-03-14T04:43:40.6827821Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:40.6830830Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_twodim_base.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:40.682848]
2025-03-14T04:43:44.1014924Z 
2025-03-14T04:43:44.1016013Z torch_np/numpy_tests/lib/test_twodim_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_twodim_base_1.1_f77f18dacc30c5c5_.log
2025-03-14T04:43:44.1016987Z Running 0 items in this shard:
2025-03-14T04:43:44.1017189Z 
2025-03-14T04:43:44.1018285Z Running lazy/test_reuse_ir 1/1 ... [2025-03-14 04:43:44.101673]
2025-03-14T04:43:44.1018720Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:44.1021988Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_reuse_ir.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:44.102011]
2025-03-14T04:43:47.5207357Z 
2025-03-14T04:43:47.5208179Z lazy/test_reuse_ir 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_reuse_ir_1.1_02a76f8af3c57e36_.log
2025-03-14T04:43:47.5209174Z Running 0 items in this shard:
2025-03-14T04:43:47.5209393Z 
2025-03-14T04:43:47.5210915Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2025-03-14 04:43:47.520919]
2025-03-14T04:43:47.5211462Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:47.5214235Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:47.521224]
2025-03-14T04:43:50.9899377Z 
2025-03-14T04:43:50.9900471Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_f9080039b28515c3_.log
2025-03-14T04:43:50.9901492Z Running 0 items in this shard:
2025-03-14T04:43:50.9901692Z 
2025-03-14T04:43:50.9902749Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2025-03-14 04:43:50.990115]
2025-03-14T04:43:50.9903283Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:50.9906346Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_index_tricks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:50.990407]
2025-03-14T04:43:54.4591442Z 
2025-03-14T04:43:54.4592544Z torch_np/numpy_tests/lib/test_index_tricks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_05bf33d88a1d6a0f_.log
2025-03-14T04:43:54.4593541Z Running 0 items in this shard:
2025-03-14T04:43:54.4593740Z 
2025-03-14T04:43:54.4594545Z Running test_type_info 1/1 ... [2025-03-14 04:43:54.459320]
2025-03-14T04:43:54.4594974Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:54.4598153Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_info.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:54.459621]
2025-03-14T04:43:57.8782117Z 
2025-03-14T04:43:57.8783000Z test_type_info 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_info_1.1_c360b24da23cbd12_.log
2025-03-14T04:43:57.8783742Z Running 0 items in this shard:
2025-03-14T04:43:57.8783946Z 
2025-03-14T04:43:57.8785355Z Running functorch/test_vmap 1/1 ... [2025-03-14 04:43:57.878385]
2025-03-14T04:43:57.8785801Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:43:57.8788672Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:43:57.878674]
2025-03-14T04:44:02.7492274Z 
2025-03-14T04:44:02.7493339Z functorch/test_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_1.1_4390da211edb08b1_.log
2025-03-14T04:44:02.7494165Z Running 0 items in this shard:
2025-03-14T04:44:02.7494368Z 
2025-03-14T04:44:02.7495466Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2025-03-14 04:44:02.749395]
2025-03-14T04:44:02.7495971Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:02.7498907Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_helper.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:02.749694]
2025-03-14T04:44:06.1683539Z 
2025-03-14T04:44:06.1684569Z torch_np/numpy_tests/fft/test_helper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_e999dc9ad1083e20_.log
2025-03-14T04:44:06.1685743Z Running 0 items in this shard:
2025-03-14T04:44:06.1685942Z 
2025-03-14T04:44:06.1686656Z Running test_type_promotion 1/1 ... [2025-03-14 04:44:06.168534]
2025-03-14T04:44:06.1687097Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:06.1690282Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_promotion.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:06.168826]
2025-03-14T04:44:09.6375600Z 
2025-03-14T04:44:09.6376459Z test_type_promotion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_promotion_1.1_d4ee9648ef9044e7_.log
2025-03-14T04:44:09.6377255Z Running 0 items in this shard:
2025-03-14T04:44:09.6377468Z 
2025-03-14T04:44:09.6378916Z Running test_linalg 1/1 ... [2025-03-14 04:44:09.637754]
2025-03-14T04:44:09.6379323Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:09.6382497Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:09.638039]
2025-03-14T04:44:14.2583394Z 
2025-03-14T04:44:14.2584246Z test_linalg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_1.1_a0f78313add1ecf4_.log
2025-03-14T04:44:14.2585875Z Running 4 items in this shard: test/test_linalg.py::TestLinalgCPU::test_svd_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_svd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_svd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_svd_cpu_float64
2025-03-14T04:44:14.2587110Z 
2025-03-14T04:44:14.2587442Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2025-03-14 04:44:14.258508]
2025-03-14T04:44:14.2587972Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:14.2590063Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:14.258796]
2025-03-14T04:44:17.6774839Z 
2025-03-14T04:44:17.6775906Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_a5bd6b5bb2feed1c_.log
2025-03-14T04:44:17.6776896Z Running 0 items in this shard:
2025-03-14T04:44:17.6777113Z 
2025-03-14T04:44:17.6778155Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2025-03-14 04:44:17.677656]
2025-03-14T04:44:17.6778698Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:17.6781521Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_methods.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:17.677951]
2025-03-14T04:44:21.1468113Z 
2025-03-14T04:44:21.1469225Z torch_np/numpy_tests/core/test_scalar_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_f1866505e3f73a20_.log
2025-03-14T04:44:21.1470232Z Running 0 items in this shard:
2025-03-14T04:44:21.1470446Z 
2025-03-14T04:44:21.1471425Z Running test_sparse 1/1 ... [2025-03-14 04:44:21.146994]
2025-03-14T04:44:21.1471832Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:21.1474765Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sparse.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:21.147292]
2025-03-14T04:44:26.0181216Z 
2025-03-14T04:44:26.0182020Z test_sparse 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_1.1_35ddde12ae7dcf28_.log
2025-03-14T04:44:26.0182771Z Running 0 items in this shard:
2025-03-14T04:44:26.0182991Z 
2025-03-14T04:44:26.0184370Z Running functorch/test_aotdispatch 4/5 ... [2025-03-14 04:44:26.018284]
2025-03-14T04:44:26.0184849Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:26.0187712Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'serial', '--shard-id=4', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:26.018580]
2025-03-14T04:44:31.1395410Z 
2025-03-14T04:44:31.1396412Z functorch/test_aotdispatch 4/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_4.5_483c30dc45aff275_.log
2025-03-14T04:44:31.1397329Z Running 0 items in this shard:
2025-03-14T04:44:31.1397578Z 
2025-03-14T04:44:31.1398800Z Running test_quantization 3/3 ... [2025-03-14 04:44:31.139702]
2025-03-14T04:44:31.1399509Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:31.1402176Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:31.140011]
2025-03-14T04:44:35.6101580Z 
2025-03-14T04:44:35.6102485Z test_quantization 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.3_72c782918dd28fa3_.log
2025-03-14T04:44:35.6103294Z Running 0 items in this shard:
2025-03-14T04:44:35.6103495Z 
2025-03-14T04:44:35.6213065Z Running dynamo/test_cudagraphs 1/1 ... [2025-03-14 04:44:35.621045]
2025-03-14T04:44:35.6213565Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:35.6216715Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:35.621464]
2025-03-14T04:44:35.6316203Z Running inductor/test_cudacodecache 1/1 ... [2025-03-14 04:44:35.631375]
2025-03-14T04:44:35.6316687Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:35.6319976Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cudacodecache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:35.631764]
2025-03-14T04:44:35.6367389Z Running inductor/test_torchinductor_strided_blocks 1/1 ... [2025-03-14 04:44:35.636504]
2025-03-14T04:44:35.6367950Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:35.6372386Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_strided_blocks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:35.637017]
2025-03-14T04:44:39.0820915Z 
2025-03-14T04:44:39.0821929Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_de9f2922b7e633d5_.log
2025-03-14T04:44:39.0822653Z 
2025-03-14T04:44:39.0822665Z 
2025-03-14T04:44:39.0823304Z inductor/test_cudacodecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudacodecache_1.1_fc98f821df678ea8_.log
2025-03-14T04:44:39.0824087Z 
2025-03-14T04:44:40.0351331Z Uploading artifacts took 0.95 seconds
2025-03-14T04:44:42.6823312Z Running inductor/test_triton_cpu_backend 1/1 ... [2025-03-14 04:44:42.681980]
2025-03-14T04:44:42.6823914Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:42.6825633Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_triton_cpu_backend.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:42.682316]
2025-03-14T04:44:42.7218030Z Running export/test_export_legacy 1/1 ... [2025-03-14 04:44:42.721461]
2025-03-14T04:44:42.7218582Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:42.7220283Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_export_legacy.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:42.721796]
2025-03-14T04:44:43.1018701Z 
2025-03-14T04:44:43.1020668Z inductor/test_torchinductor_strided_blocks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_strided_blocks_1.1_931c0a9637eaedc5_.log
2025-03-14T04:44:43.1022364Z 
2025-03-14T04:44:46.7345450Z Running inductor/test_aot_inductor_custom_ops 1/1 ... [2025-03-14 04:44:46.734167]
2025-03-14T04:44:46.7346231Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:46.7347884Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_aot_inductor_custom_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:46.734482]
2025-03-14T04:44:50.1140875Z 
2025-03-14T04:44:50.1141922Z inductor/test_triton_cpu_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_cpu_backend_1.1_dffa25c76c920585_.log
2025-03-14T04:44:50.1142728Z 
2025-03-14T04:44:51.3743320Z 
2025-03-14T04:44:51.3744253Z export/test_export_legacy 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_export_legacy_1.1_0b275fc45c0dbbb9_.log
2025-03-14T04:44:51.3744991Z 
2025-03-14T04:44:53.8106458Z Running inductor/test_torchinductor_dynamic_shapes 4/4 ... [2025-03-14 04:44:53.810276]
2025-03-14T04:44:53.8107061Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:53.8108866Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_dynamic_shapes.py', '-m', 'not serial', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:53.810646]
2025-03-14T04:44:54.2291296Z 
2025-03-14T04:44:54.2293752Z inductor/test_aot_inductor_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_aot_inductor_custom_ops_1.1_e4ee6e56c7e75fdf_.log
2025-03-14T04:44:54.2295464Z 
2025-03-14T04:44:54.9871844Z Running inductor/test_op_dtype_prop 1/1 ... [2025-03-14 04:44:54.986819]
2025-03-14T04:44:54.9872458Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:54.9874141Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_op_dtype_prop.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:54.987154]
2025-03-14T04:44:57.9132334Z Running inductor/test_pattern_matcher 1/1 ... [2025-03-14 04:44:57.912822]
2025-03-14T04:44:57.9133278Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:44:57.9137551Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_pattern_matcher.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:44:57.913410]
2025-03-14T04:45:03.1156804Z 
2025-03-14T04:45:03.1158379Z inductor/test_op_dtype_prop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_op_dtype_prop_1.1_47d758bba41ddd30_.log
2025-03-14T04:45:03.1159863Z 
2025-03-14T04:45:05.1284942Z 
2025-03-14T04:45:05.1286694Z inductor/test_pattern_matcher 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_pattern_matcher_1.1_651a82ab29f03e64_.log
2025-03-14T04:45:05.1287945Z 
2025-03-14T04:45:06.7540134Z Running inductor/test_torchinductor_codegen_dynamic_shapes 2/4 ... [2025-03-14 04:45:06.753630]
2025-03-14T04:45:06.7540759Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:45:06.7542362Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'not serial', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:45:06.753979]
2025-03-14T04:45:08.7708348Z Running inductor/test_torchinductor_codegen_dynamic_shapes 3/4 ... [2025-03-14 04:45:08.770425]
2025-03-14T04:45:08.7709374Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:45:08.7711016Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'not serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:45:08.770751]
2025-03-14T04:52:47.2908287Z 
2025-03-14T04:52:47.2910257Z inductor/test_torchinductor_codegen_dynamic_shapes 3/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_3.4_ae10da97519ceaf5_.log
2025-03-14T04:52:47.3150773Z Running 207 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_max_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_pool_errors_with_long_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_addmv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aliased_buffer_reuse_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin_with_duplicates_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_min_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool3d_backward3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool3d_backward4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool3d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool_errors_with_uint_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_baddbmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bernoulli1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bernoulli2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bfloat16_to_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bmm2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_both_scalars_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_computed_offsets_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_default_kwargs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_nd_tiling_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_copied_in_graph_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_int_ndigits_pos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_int_ndigits_zero_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_empty_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_unbacked_2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_chunk_recompiles_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_complex_memory_overlap_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_config_option_dont_assume_alignment_recompiles_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_consecutive_split_cumprod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_fill_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_nd_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv2d_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv3d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_no_mask_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_pattern_matcher_issue_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_default_layout_constraint_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_would_split_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_deterministic_codegen_with_suffix_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_diagonal_copy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dist_bf16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_trivial_1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtype_mismatch_issue_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_exact_stride_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_basic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_list_tensor_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_flip_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float32_to_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fuse_tiled_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gather1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gather_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gelu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_constant_tensor2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_no_inputs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_unbacked_symint_as_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_grid_sampler_2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_hardtanh_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_horizonal_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_horizonal_fusion2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_flip_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_as_masked_fill_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_select_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_indirect_load_broadcast_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_isin_tensor_scalar_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_l1_loss_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_pointwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_lerp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear_dynamic_maxautotune_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linspace1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_logaddexp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_logsumexp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_masked_fill_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_masked_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_matmul_layer_norm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_min_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mixed_mm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mul_index_expr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multi_gpu_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_prime_size_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_var_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_var_lowp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mutable_custom_op_fixed_layout_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_new_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_no_mega_fusion_during_lowering_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nonzero_unbacked_refinement_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_output_strides_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pattern_matcher_multi_user_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_permute2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_j1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_t_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_v_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_digamma_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erfcx_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erfinv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_expit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_gammainc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_laguerre_polynomial_l_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_legendre_polynomial_p_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_log1p_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_modified_bessel_k0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_psi_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_round_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_shifted_chebyshev_polynomial_u_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_xlog1py_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow_symfloat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_profiler_mark_wrapper_call_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randint_kernel_count_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randn_generator_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randn_like_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reflection_pad2d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_noop_clone_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_noop_copy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_interleave_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reuse_buffers_with_aliasing_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scalar_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scaled_dot_product_attention_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_add1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_reduce1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_reduce2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_searchsorted_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sgn_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_shape_prop_torch_ones_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_silu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_simplify_loops_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_single_elem_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sizehint_issue1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_view_with_graph_break_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sort_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_cumsum_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_with_integer_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sqrt_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum_keepdims_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue1_use_block_ptr_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_device_constant_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_memory_format_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transpose_add_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_bilinear2d_a_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_bilinear2d_b_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_cat_conv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_nearest3d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vertical_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_as_real_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_uint8_through_differing_bitwidths_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_zero_dim_reductions_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_zeros_dynamic_shapes_cpu
2025-03-14T04:52:47.3379093Z 
2025-03-14T04:52:50.9383968Z Running inductor/test_torchinductor_codegen_dynamic_shapes 4/4 ... [2025-03-14 04:52:50.937989]
2025-03-14T04:52:50.9384862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:52:50.9386601Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'not serial', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:52:50.938340]
2025-03-14T04:52:59.4566843Z 
2025-03-14T04:52:59.4568587Z inductor/test_torchinductor_dynamic_shapes 4/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_dynamic_shapes_4.4_281b313d7c160ede_.log
2025-03-14T04:52:59.4806672Z Running 205 items in this shard: test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test__dyn_quant_pack_4bit_weight_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test__unsafe_masked_index_put_accumulate_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_abs_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_adaptive_avg_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_adaptive_avg_pool_errors_with_long_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_adding_tensor_offsets_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_aoti_eager_override_registration_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_aoti_eager_support_out_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_arange3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_as_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_avg_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_avg_pool2d5_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_avg_pool2d7_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_avg_pool2d_backward3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_batch_norm_2d_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_bfloat16_to_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_bitwise3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_bmm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_both_scalars_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_bucketize_add_autotune_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_bucketize_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_buffer_copied_in_graph_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_builtins_round_float_ndigits_pos_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cat_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cat_extern_kernel_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cat_unbacked_legacy_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cauchy_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_clamp_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_clamp_type_promotion_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_concat_add_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_config_option_dont_assume_alignment_cudagraphs_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_const_int32_to_float_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_conv2d_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_conv_functional_bn_fuse_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_conv_inference_heuristics_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_convolution1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_convolution2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_convolution3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_convolution4_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cumprod_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cumsum_pattern_matcher_issue_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_cumsum_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_custom_op_fixed_layout_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_div3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_div6_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_div_by_zero_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_div_prim_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dropout_trivial_0_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dropout_trivial_1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_float16_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_float32_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_float32_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_float32_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_float32_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_float64_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_float64_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int16_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int16_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int32_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int32_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int64_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int64_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int64_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_int8_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_uint8_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_dtypeview_uint8_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_elu_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_empty2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_exact_stride_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_expand_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_expm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_fft_real_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_fft_real_input_real_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_fill1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_flip_cat_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_float16_to_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_fmod_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_forced_buffer_realize_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_fractional_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_fuse_large_params_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_generate_rand_fp8_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_getitem_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_glu_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_graph_partition_arange2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_grid_sampler_2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_horizonal_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_index_propagation_abs_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_index_put2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_index_put4_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_index_put_reinplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_indirect_load_broadcast_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_inductor_assert_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_inf_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_inplace_add_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_inplace_where_pointwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_input_mutation5_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_insignificant_strides_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_isinf2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_kernel_names_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_large_broadcast_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_large_strided_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_like_rands2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_linear1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_linear2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_linspace4_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_list_clearing_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_masked_fill_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_matmul_layer_norm_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_max_pool2d5_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_max_pool2d8_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_mix_device_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_mul_index_expr_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_multi_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_multilayer_any_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_mutable_custom_op_fixed_layout2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_nan_to_num_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_narrow_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_new_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_new_ones_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_no_op_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_no_specization_over_symbolic_value_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_output_strides_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pad_view_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_permute1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pixel_shuffle_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_chebyshev_polynomial_u_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_expm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_gammaln_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_i0_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_i0e_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_i1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_i1e_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_modified_bessel_k0_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_psi_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_shifted_chebyshev_polynomial_v_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pointwise_spherical_bessel_j0_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pow1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_pow2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_randint_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_randint_int64_mod_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_randn_generator_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_reduction5_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_reflection_pad2d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_reflection_pad2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_relu_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_roi_align_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_rsqrt_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_rsqrt_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_scatter1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_scatter2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_scatter6_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_scatter_add3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_scatter_bf16_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_scheduler_vertical_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_sdpa_prefer_nd_tiling_False_use_block_ptr_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_sdpa_unaligned_mask_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_select_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_setitem_with_int_parameter_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_sigmoid_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_silu_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_simplify_loops_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_sizehint_issue1_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_slice2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_slice3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_slice_scatter2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_slice_scatter4_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_slice_scatter_reinplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_slice_view_with_graph_break_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_softmax_one_kernel_loop_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_softmax_one_kernel_persist_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_split_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_sum2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_sum3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_sum_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_tensor_index_put_slice_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_tmp_not_defined_issue1_use_block_ptr_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_tmp_not_defined_issue2_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_to_memory_format_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_triu_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_unfold_zero_dimension_tensor_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_unroll_small_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_unspec_inputs_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_unspec_inputs_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_var_mean_tile_reduction_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_view_as_complex_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_view_as_real_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_views3_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_weight_norm_bwd_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_where_with_logical_op_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::DynamicShapesCpuTests::test_zero_element_mutation_dynamic_shapes_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_cat_unbacked_duplicate_size_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_float_item_inf_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_item_materialize_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_item_return_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_math_ops_op0_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_math_ops_op2_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_nonzero_no_realloc_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_nonzero_size_factory_nobreak_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_return_unbacked_view_split_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_slice_index_changing_sign_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_sym_stride_lowering_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_sym_sum_unbacked_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_unspecialized_float_fallback_specialization_cpu, test/inductor/test_torchinductor_dynamic_shapes.py::TestInductorDynamicCPU::test_unwrap_storage_didnt_work_repro_cpu
2025-03-14T04:52:59.4923431Z 
2025-03-14T04:53:03.0070970Z Running inductor/test_cpu_select_algorithm 5/16 ... [2025-03-14 04:53:03.006727]
2025-03-14T04:53:03.0071587Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:53:03.0073175Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=5', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:53:03.007060]
2025-03-14T04:53:05.0647022Z 
2025-03-14T04:53:05.0648612Z inductor/test_torchinductor_codegen_dynamic_shapes 2/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.4_9289ec4bb8344b9a_.log
2025-03-14T04:53:05.0763374Z Running 196 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_AllenaiLongformerBase_repro_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__unsafe_masked_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__unsafe_masked_index_put_accumulate_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_inplace_permuted_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adding_tensor_offsets_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_alexnet_prefix_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_angle_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_any_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_cache_hit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_with_persistent_cache_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_as_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d_backward4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bmm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_batch_norm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_use_after_remove_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_float_ndigits_neg_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_extern_kernel_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_unbacked_legacy_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cauchy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_check_stack_no_cycles_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clamp_type_promotion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_concat_add_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_consecutive_split_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_1d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv3d_channels_last_use_block_ptr_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_inference_heuristics_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_with_as_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumprod_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_compiled_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_deterministic_codegen_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_deterministic_codegen_on_graph_break_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_trivial_0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtype_sympy_expr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_fusion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_elu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_erfinv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expanded_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_with_return_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fill1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_floordiv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmod_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_forced_buffer_realize_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_getitem_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_glu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_arange1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_arange2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_both_scalars_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_misaligned_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_hardsigmoid_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_hardswish_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_floordiv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_remainder_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_fallback1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_fallback2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_reinplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_activations_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_add_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_mixed_dtype_ops_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_resize_as_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_where_pointwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_int_input_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_issue102546_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_broadcast_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_grid_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_offset_pointwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_layer_norm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_leaky_relu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_list_clearing_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log1p_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log_fp64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_logcumsumexp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_min_max_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_min_max_reduction_nan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mixed_mm2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mixed_mm3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mm_mixed_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_sum_low_prec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mutable_custom_op_fixed_layout2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_neg_max_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_new_empty_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nll_loss_forward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_one_hot_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pad_cast_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_j0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erfc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_exp2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_expm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_gammaincc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_modified_bessel_i0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_multigammaln_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_ndtr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_polygamma_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_scaled_modified_bessel_k1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_shifted_chebyshev_polynomial_v_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_xlogy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_prepare_softmax_with_fast_math_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_rand_like_deterministic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction_config_limit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remainder_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_as_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_resize_as_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_round_correctness_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_rsqrt_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scalar_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scaled_dot_product_efficient_attention_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_bf16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scheduler_vertical_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_setitem_with_int_parameter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sgn_extremal_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_should_pad_bench_for_bmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sigmoid_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sign_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sin_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_backward_data_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_one_kernel_persist_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sort_stable_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze_varargs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue1_use_block_ptr_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_triu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_uint_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unbacked_floordiv_simplify_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unbacked_floordiv_simplify_errors_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unbind_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unsqueeze_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_nearest2d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_correction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_mean_tile_reduction_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_where_broadcast_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_xblock_divides_xnumel_dynamic_shapes_cpu
2025-03-14T04:53:05.0875754Z 
2025-03-14T04:53:08.7143355Z Running inductor/test_cpu_select_algorithm 13/16 ... [2025-03-14 04:53:08.713903]
2025-03-14T04:53:08.7144340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T04:53:08.7146552Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=13', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 04:53:08.714286]
2025-03-14T05:01:28.7770002Z 
2025-03-14T05:01:28.7771465Z inductor/test_torchinductor_codegen_dynamic_shapes 4/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_4.4_88d59720bcf1f338_.log
2025-03-14T05:01:28.7878320Z Running 183 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__dyn_quant_matmul_4bit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__dyn_quant_pack_4bit_weight_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool2d_low_prec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool_errors_with_long_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_max_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_const_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_addmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_dtype_device_layout_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_override_registration_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_support_str_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin_with_nan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_to_float_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_batch_norm_2d_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bool_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_add_autotune_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_nd_tiling_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_copied_in_graph_with_different_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_float_ndigits_pos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_single_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clamp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clone_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_complex_fallback_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_computed_buffer_inlining_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_config_option_dont_assume_alignment_cudagraphs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_3d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv2d_backward_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_shape_check_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_inf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_fixed_layout_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_unbacked_symints_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_multi_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dense_mask_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dist_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_precision_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_prim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_softmax_symfloat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_deterministic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_erfc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expand_as_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_no_mutated_tensors_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fft_real_input_real_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fill2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_flip_cat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float16_to_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float_index_expression_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float_index_expression_type_promotion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_boolean_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_like_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_truncation_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fuse_large_params_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fusing_write_into_disjoint_read_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gather3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_generate_rand_fp8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_argmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_constant_tensor1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_refcount_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_scalar_inputs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_device_assert_masked_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_nested_indirect_indexing_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_deterministic_fallback_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_tensor_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inductor_assert_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inductor_layout_optimization_input_mutations_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_insignificant_strides_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_isinf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_kernel_names_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_kwargs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_strided_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_tensor_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear_mixed_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linspace2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log_softmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_low_memory_max_pool_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mm_views_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mul_softmax_symfloat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multi_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_any_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mutations_loop_fusion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_neg_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_new_ones_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nll_loss_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pad_single_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pad_view_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_y0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_u_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_w_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_hermite_polynomial_h_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i0e_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i1e_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_logit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_sinc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_spherical_bessel_j0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_zeta_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randint_distribution_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randn_with_dtype_and_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_relu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_no_ops_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_resize_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_roi_align_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_add3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_reduce3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_unaligned_mask_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_single_elem_indirect_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_size_asserts_for_multi_output_fallback_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_mutation1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_mutation2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_one_kernel_loop_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tanh_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor_index_put_slice_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transpose_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transposed_propagates_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unsqueeze_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_bicubic2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_nearest1d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_mean_tile_reduction_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vectorized_ops_masked_var_novec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_as_complex_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_detach_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_weight_norm_bwd_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_where_with_logical_op_dynamic_shapes_cpu
2025-03-14T05:01:28.7983578Z 
2025-03-14T05:01:32.5507997Z Running inductor/test_cpu_select_algorithm 15/16 ... [2025-03-14 05:01:32.550373]
2025-03-14T05:01:32.5508946Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:01:32.5511151Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=15', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:01:32.550732]
2025-03-14T05:02:01.2643986Z 
2025-03-14T05:02:01.2645062Z inductor/test_cpu_select_algorithm 13/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_13.16_94ed6e4b35273872_.log
2025-03-14T05:02:01.2724637Z Running 101 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order1_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order4_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amp_bs_1_Mdim_192_Kdim_196_Ndim_84_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_16_Kdim_32_Ndim_1025_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_1025_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_16_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_64_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_64_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_64_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_reshape_a_False_expand_a_scale_True_dynamic_True_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_True_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_False_reshape_a_True_expand_a_scale_True_dynamic_False_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_True_reshape_a_False_expand_a_scale_True_dynamic_False_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_reshape_a_False_expand_a_scale_False_dynamic_False_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_True_M_32_cpu_bfloat16, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_cache_blocking_batch_size_512_in_features_1024_out_features_1024_bias_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_out_features_1_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1_out_features_1024_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1_out_features_1_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_out_features_1024_bias_True_input_3d_True_cpu_float32, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_div_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardsigmoid_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardswish_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_sigmoid_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_silu_cpu_float16, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_385_bias_False_unary_relu_binary_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_385_bias_False_unary_relu_binary_mul_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_div_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_16_in_features_68_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_4_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_4_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_68_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_68_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_3_in_features_4_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_49_in_features_128_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_epilogue_gelu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_int8_mixed_bf16_False_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_with_pointwise_with_reshape_dynamic_shapes_bs_5_Mdim_384_Kdim_96_Ndim_65_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_False_bias1_epilogue3_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias2_epilogue4_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias3_epilogue4_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1000_out_features_1_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1000_out_features_1_bias_True_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1024_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1_bias_True_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1000_out_features_1_bias_False_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1000_out_features_1_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1024_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1024_bias_True_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_k_slicing_dynamic_shapes_batch_size_2_in_features_1000_out_features_2_bias_True_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardswish_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_gelu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_tanh_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardswish_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_sub_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_unary_relu_binary_div_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_unary_relu_binary_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_128_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_128_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_32_in_features_4_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_49_in_features_128_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_49_in_features_4_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_int8_mixed_bf16_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_gelu_cpu_float32
2025-03-14T05:02:01.2801468Z 
2025-03-14T05:02:04.9940841Z Running inductor/test_torchinductor_opinfo 3/42 ... [2025-03-14 05:02:04.993720]
2025-03-14T05:02:04.9941401Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:02:04.9943772Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=3', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:02:04.994104]
2025-03-14T05:02:20.7509248Z 
2025-03-14T05:02:20.7510966Z inductor/test_cpu_select_algorithm 5/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_5.16_4d55182aa1d3e29a_.log
2025-03-14T05:02:20.7586895Z Running 93 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order0_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_1024_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_3_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_48_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_bs_1_Mdim_192_Kdim_196_Ndim_385_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_fused_epilogues_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_True_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_True_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_True_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_True_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_True_expand_a_scale_True_dynamic_False_M_1_cpu_float32, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_sub_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_tanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_add_cpu_float16, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_int8_mixed_bf16_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_with_pointwise_dynamic_shapes_bs_5_Mdim_384_Kdim_96_Ndim_64_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias0_epilogue0_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias1_epilogue2_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias2_epilogue2_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1000_out_features_1_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1000_out_features_1_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_float32, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_k_slicing_dynamic_shapes_batch_size_2_in_features_1000_out_features_2_bias_False_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardtanh_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_silu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_gelu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_div_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardsigmoid_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_leaky_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_sub_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_3_in_features_4_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_49_in_features_4_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_49_in_features_4_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_int8_mixed_bf16_False_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_epilogue_none_cpu_bfloat16
2025-03-14T05:02:20.7659089Z 
2025-03-14T05:02:24.5246870Z Running inductor/test_torchinductor_opinfo 10/42 ... [2025-03-14 05:02:24.524306]
2025-03-14T05:02:24.5247475Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:02:24.5249948Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=10', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:02:24.524710]
2025-03-14T05:09:30.9206895Z 
2025-03-14T05:09:30.9208108Z inductor/test_cpu_select_algorithm 15/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_15.16_e6e64e02dca6d3b2_.log
2025-03-14T05:09:30.9281468Z Running 89 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order3_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_bs_1_Mdim_192_Kdim_196_Ndim_84_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_self_permute_bs_5_Mdim_64_Kdim_96_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_65_epilogue_div_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_True_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_True_reshape_a_False_expand_a_scale_False_dynamic_False_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_False_expand_a_scale_False_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_False_dynamic_False_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_False_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_False_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_True_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_True_reshape_a_True_expand_a_scale_True_dynamic_True_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_grouped_linear_epilogue_batch_size_16_in_features_52_out_features_32_input_3d_False_bias1_epilogue3_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_input_transpose_bias_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_out_features_1_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1_out_features_1_bias_True_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1000_out_features_1024_bias_True_input_3d_False_cpu_bfloat16, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_epilogue_gelu_cpu_bfloat16
2025-03-14T05:09:30.9351054Z 
2025-03-14T05:09:31.5663044Z Uploading artifacts took 0.64 seconds
2025-03-14T05:09:34.5846570Z Running inductor/test_torchinductor_opinfo 13/42 ... [2025-03-14 05:09:34.584318]
2025-03-14T05:09:34.5847318Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:09:34.5849468Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=13', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:09:34.584649]
2025-03-14T05:13:41.3398604Z 
2025-03-14T05:13:41.3400408Z inductor/test_torchinductor_opinfo 10/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_10.42_375586ff233ac151_.log
2025-03-14T05:13:41.3491297Z Running 96 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmod___cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmv_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_angle_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_any_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bool_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_broadcast_tensors_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cfloat_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_physical_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_scatter_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_double_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_einsum_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp2_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftn_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftn_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfftn_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_irfft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gradient_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hsplit_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_i0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_igamma_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isreal_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_return_by_ref_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_le_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cholesky_ex_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_qr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_tensorinv_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log1p_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_xor_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logit_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lt_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_argmin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumprod_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_select_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_no_dim_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nansum_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_max_pool3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cosine_embedding_loss_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_elu_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_embedding_bag_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_embedding_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_fractional_max_pool3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_group_norm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest-exact_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_local_response_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_multi_head_attention_forward_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_nll_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_reflect_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_replicate_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_replicate_negative_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ones_like_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_0_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_put_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_interleave_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_roll_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_round_decimals_3_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_general_cosine_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinc_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_j0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y1_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i1_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_legendre_polynomial_p_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_log_ndtr_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtri_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_along_dim_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_sparse_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_torch_ops_aten__safe_softmax_default_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_consecutive_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_var_cpu_float64
2025-03-14T05:13:41.3583312Z 
2025-03-14T05:13:44.9252395Z Running inductor/test_torchinductor_opinfo 18/42 ... [2025-03-14 05:13:44.924815]
2025-03-14T05:13:44.9253066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:13:44.9254713Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=18', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:13:44.925171]
2025-03-14T05:13:46.3440008Z 
2025-03-14T05:13:46.3441595Z inductor/test_torchinductor_opinfo 3/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_3.42_4b9e44ce6f90a9bc_.log
2025-03-14T05:13:46.3490126Z Running 95 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___getitem___cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rdiv___cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___ror___cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__batch_norm_with_update_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_aminmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bernoulli_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ceil_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_min_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_contiguous_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_count_nonzero_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cross_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumulative_trapezoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_deg2rad_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_deg2rad_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dot_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expm1_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_irfft_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmod_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_histc_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_multi_dot_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_normal_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logit_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_no_dim_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_with_dim_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_no_dim_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_movedim_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmean_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_dropout_backward_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool3d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_elu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_embedding_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bilinear_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest-exact_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_pool1d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_multi_head_attention_forward_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_scaled_dot_product_attention_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_selu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_upsample_bilinear_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_positive_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_like_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_prod_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_hermite_polynomial_h_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i1e_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_list_args_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_square_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_square_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_along_dim_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_transpose_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_indices_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_var_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vdot_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_uint8
2025-03-14T05:13:46.3537211Z 
2025-03-14T05:13:50.0522850Z Running inductor/test_torchinductor_opinfo 25/42 ... [2025-03-14 05:13:50.051889]
2025-03-14T05:13:50.0523584Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:13:50.0525444Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=25', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:13:50.052237]
2025-03-14T05:18:51.3999869Z 
2025-03-14T05:18:51.4001577Z inductor/test_torchinductor_opinfo 13/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_13.42_65597230cd3b48dc_.log
2025-03-14T05:18:51.4074359Z Running 76 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rsub___cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_alias_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_alias_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_aminmax_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asinh_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_2d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bincount_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_broadcast_shapes_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cartesian_prod_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cfloat_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_count_nonzero_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cov_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_digamma_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erf_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftshift_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmod_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gt_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_householder_product_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_matrix_rank_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_pinv_singular_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logaddexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_and_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_long_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mT_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_select_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_movedim_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_binary_cross_entropy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bicubic_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_local_response_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_local_response_norm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_poisson_nll_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softsign_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_1_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_pow_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_gaussian_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_laguerre_polynomial_l_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_square_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_stack_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_svd_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensordot_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_torch_ops_aten__safe_softmax_default_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_indices_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unbind_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsafe_chunk_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsafe_chunk_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vdot_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_as_complex_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_xlogy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_cpu_float32
2025-03-14T05:18:51.4145713Z 
2025-03-14T05:18:55.1152396Z Running inductor/test_torchinductor_opinfo 28/42 ... [2025-03-14 05:18:55.114812]
2025-03-14T05:18:55.1153348Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:18:55.1155458Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=28', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:18:55.115161]
2025-03-14T05:22:08.8906808Z 
2025-03-14T05:22:08.8907915Z inductor/test_torchinductor_opinfo 25/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_25.42_3447e81bf470cc41_.log
2025-03-14T05:22:08.8944891Z Running 75 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_add_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_alias_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_partial_views_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_block_diag_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_block_diag_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bmm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_broadcast_tensors_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_combinations_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cosh_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cross_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfft_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_frac_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_igammac_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_select_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isneginf_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_2inputs_2outputs_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_kron_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_matrix_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_solve_ex_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumprod_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumsum_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_logsumexp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_mean_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mv_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_dropout_backward_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_strided_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool2d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv2d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv_transpose3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_huber_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_replicate_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_shuffle_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polar_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_prod_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reciprocal_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_interleave_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_neg_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rot90_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_amax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_scatter_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_t_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_hermite_polynomial_h_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_laguerre_polynomial_l_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_list_args_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sqrt_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensor_split_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_topk_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_uint16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_like_cpu_uint8
2025-03-14T05:22:08.8981422Z 
2025-03-14T05:22:12.8410796Z Running inductor/test_torchinductor_opinfo 33/42 ... [2025-03-14 05:22:12.840665]
2025-03-14T05:22:12.8411758Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:22:12.8414154Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=33', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:22:12.841074]
2025-03-14T05:24:04.1089277Z 
2025-03-14T05:24:04.1090549Z inductor/test_torchinductor_opinfo 18/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_18.42_23f3c2d0645fba89_.log
2025-03-14T05:24:04.1131679Z Running 83 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___radd___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__chunk_cat_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addcdiv_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addr_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_allclose_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_partial_views_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bincount_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_and_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clone_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_contiguous_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cos_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummin_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diff_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expm1_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_irfftn_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flipud_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_histogram_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_return_by_ref_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ldexp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_norm_subgradients_at_zero_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logcumsumexp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumsum_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_log_softmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_scatter_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_softmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_var_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_matrix_exp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_median_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_binary_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_ones_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_bilinear_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cross_entropy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_huber_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_area_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_l1_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool1d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool3d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_circular_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_unshuffle_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_poisson_nll_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softmin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_tanhshrink_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_tanhshrink_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_in_place_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_roll_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_legendre_polynomial_p_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_multiple_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_topk_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unbind_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unravel_index_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_xlogy_cpu_bool
2025-03-14T05:24:04.1172218Z 
2025-03-14T05:24:07.6638679Z Running inductor/test_torchinductor_opinfo 40/42 ... [2025-03-14 05:24:07.663482]
2025-03-14T05:24:07.6639354Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:24:07.6640989Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=40', '--num-shards=42', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:24:07.663818]
2025-03-14T05:25:27.8974098Z 
2025-03-14T05:25:27.8975861Z inductor/test_torchinductor_opinfo 28/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_28.42_67f2cbe07bb8357d_.log
2025-03-14T05:25:27.9057099Z Running 85 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_T_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rsub___cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmm_decomposed_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_allclose_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_block_diag_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bool_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_char_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_combinations_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_physical_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumulative_trapezoid_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_double_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_einsum_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfft_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_divide_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_igamma_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_mean_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isneginf_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_return_by_ref_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_kthvalue_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lcm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_ldl_factor_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_ldl_factor_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_multi_dot_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_solve_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vander_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vector_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log10_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logcumsumexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_or_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_xor_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mT_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_fill_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_scatter_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_select_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_pool2d_with_indices_backward_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_no_dim_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_no_dim_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_msort_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nan_to_num_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_bilinear_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout2d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardshrink_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_instance_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_shuffle_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nonzero_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nonzero_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_inf_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_outer_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_1_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_round_decimals_3_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsub_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_amin_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_mean_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_bartlett_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_general_hamming_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_nuttall_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sparse_mm_reduce_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_w_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k0_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k0_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapz_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trunc_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unflatten_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsafe_split_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_as_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_like_cpu_float64
2025-03-14T05:25:27.9136433Z 
2025-03-14T05:25:31.4924434Z Running inductor/test_block_analysis 1/1 ... [2025-03-14 05:25:31.492040]
2025-03-14T05:25:31.4925020Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:25:31.4926752Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_block_analysis.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:25:31.492393]
2025-03-14T05:25:38.8687717Z 
2025-03-14T05:25:38.8688968Z inductor/test_block_analysis 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_block_analysis_1.1_918d81f1e30d8b84_.log
2025-03-14T05:25:38.8694444Z Running 10 items in this shard: test/inductor/test_block_analysis.py::BlockAnalysisTest::test_affine_identity_stride_3_symbol2_expr2, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_affine_identity_stride_4_symbol1_expr1, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_affine_identity_stride_5_symbol0_expr0, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_index_with_dynamic_shapes, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_mod_div_identity_dims0_strides0_symbol0_expr0, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_mod_div_identity_dims1_strides1_symbol1_expr1, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_mod_div_identity_dims2_strides2_symbol2_expr2, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_subexpr_identity_symbol0_expr0_subexpr0, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_subexpr_identity_symbol1_expr1_subexpr1, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_subexpr_identity_symbol2_expr2_subexpr2
2025-03-14T05:25:42.5531421Z 
2025-03-14T05:25:42.5531978Z Running dynamo/test_sources 1/1 ... [2025-03-14 05:25:42.552785]
2025-03-14T05:25:42.5532571Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:25:42.5534033Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:25:42.553170]
2025-03-14T05:25:46.3237437Z 
2025-03-14T05:25:46.3238445Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_9607101f80b8ce43_.log
2025-03-14T05:25:46.3240055Z Running 3 items in this shard: test/dynamo/test_sources.py::SourceTests::test_is_local, test/dynamo/test_sources.py::SourceTests::test_property_closure, test/dynamo/test_sources.py::SourceTests::test_supported_nodes
2025-03-14T05:25:46.3240993Z 
2025-03-14T05:25:49.9051588Z Running inductor/test_fuzzer 1/1 ... [2025-03-14 05:25:49.904800]
2025-03-14T05:25:49.9052134Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:25:49.9053861Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fuzzer.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:25:49.905152]
2025-03-14T05:25:59.5333083Z 
2025-03-14T05:25:59.5334046Z inductor/test_fuzzer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fuzzer_1.1_cb6fd081e0502215_.log
2025-03-14T05:25:59.5338710Z Running 11 items in this shard: test/inductor/test_fuzzer.py::TestConfigFuzzer::test_config_fuzzer_bisector_boolean, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_config_fuzzer_bisector_exception, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_config_fuzzer_dynamo_bisect, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_config_fuzzer_inductor_bisect, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_config_fuzzer_inductor_cpu, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_config_fuzzer_inductor_gpu, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_config_fuzzer_n_tuple, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_fuzzer_inductor_calling_compile, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_fuzzer_running_test, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_sampling_method_random, test/inductor/test_fuzzer.py::TestConfigFuzzer::test_sampling_method_toggle
2025-03-14T05:25:59.5342529Z 
2025-03-14T05:26:03.1185735Z Running inductor/test_custom_lowering 1/1 ... [2025-03-14 05:26:03.118152]
2025-03-14T05:26:03.1186768Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:26:03.1189238Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_custom_lowering.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:26:03.118508]
2025-03-14T05:26:10.5439800Z 
2025-03-14T05:26:10.5440895Z inductor/test_custom_lowering 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_custom_lowering_1.1_6db08fdd64843bc8_.log
2025-03-14T05:26:10.5443391Z Running 4 items in this shard: test/inductor/test_custom_lowering.py::TestCustomLowering::test_jagged_to_padded_dense_sanity_cuda, test/inductor/test_custom_lowering.py::TestCustomLowering::test_jagged_to_padded_dense_zero_size, test/inductor/test_custom_lowering.py::TestCustomLowering::test_multi_inp_asm, test/inductor/test_custom_lowering.py::TestCustomLowering::test_tanh_approx
2025-03-14T05:26:10.5445184Z 
2025-03-14T05:26:14.2257935Z Running dynamo/test_recompiles 1/1 ... [2025-03-14 05:26:14.225449]
2025-03-14T05:26:14.2258519Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:26:14.2260077Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:26:14.225783]
2025-03-14T05:26:19.2468130Z 
2025-03-14T05:26:19.2469452Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_a8719c7072ff009a_.log
2025-03-14T05:26:19.2476320Z Running 15 items in this shard: test/dynamo/test_recompiles.py::RecompileTests::test_aliasing_guard_failures, test/dynamo/test_recompiles.py::RecompileTests::test_aliasing_guard_failures_with_globals, test/dynamo/test_recompiles.py::RecompileTests::test_ambient_autocast_recompile, test/dynamo/test_recompiles.py::RecompileTests::test_autocast_constant_fold, test/dynamo/test_recompiles.py::RecompileTests::test_automatic_dynamic_on_closed_ints, test/dynamo/test_recompiles.py::RecompileTests::test_automatic_dynamic_reduce_recompiles, test/dynamo/test_recompiles.py::RecompileTests::test_automatic_dynamic_shapes_mark_as_oblivious, test/dynamo/test_recompiles.py::RecompileTests::test_automatic_dynamic_shapes_mark_as_oblivious_fail_counterfactual, test/dynamo/test_recompiles.py::RecompileTests::test_automatic_dynamic_shapes_mark_as_unbacked, test/dynamo/test_recompiles.py::RecompileTests::test_automatic_dynamic_tensor_scalar_change, test/dynamo/test_recompiles.py::RecompileTests::test_dynamic_shape_parameter_recompile, test/dynamo/test_recompiles.py::RecompileTests::test_no_recursive_compile_after_cache_limit_hit, test/dynamo/test_recompiles.py::RecompileTests::test_recompiles_true_false_flop, test/dynamo/test_recompiles.py::RecompileTests::test_run_mode_after_cache_limit_hit, test/dynamo/test_recompiles.py::RecompileTests::test_simple_module_recompile
2025-03-14T05:26:19.2482340Z 
2025-03-14T05:26:22.7972186Z Running test_namedtensor 1/1 ... [2025-03-14 05:26:22.796858]
2025-03-14T05:26:22.7972697Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:26:22.7974033Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtensor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:26:22.797179]
2025-03-14T05:26:26.8176881Z 
2025-03-14T05:26:26.8178261Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_82bc74a32dfb42dc_.log
2025-03-14T05:26:26.8226199Z Running 88 items in this shard: test/test_namedtensor.py::TestNamedTensor::test_aaa_must_run_first_check_experimental_warning, test/test_namedtensor.py::TestNamedTensor::test_addcmul_addcdiv, test/test_namedtensor.py::TestNamedTensor::test_addmm, test/test_namedtensor.py::TestNamedTensor::test_addmv, test/test_namedtensor.py::TestNamedTensor::test_align_as, test/test_namedtensor.py::TestNamedTensor::test_align_tensors, test/test_namedtensor.py::TestNamedTensor::test_align_tensors_two_inputs, test/test_namedtensor.py::TestNamedTensor::test_align_to, test/test_namedtensor.py::TestNamedTensor::test_align_to_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_any_all, test/test_namedtensor.py::TestNamedTensor::test_as_strided, test/test_namedtensor.py::TestNamedTensor::test_as_strided_cuda, test/test_namedtensor.py::TestNamedTensor::test_autograd_ignores_names, test/test_namedtensor.py::TestNamedTensor::test_autograd_smoke, test/test_namedtensor.py::TestNamedTensor::test_autograd_warns_named_grad, test/test_namedtensor.py::TestNamedTensor::test_bernoulli, test/test_namedtensor.py::TestNamedTensor::test_big_tensor_repr_has_names, test/test_namedtensor.py::TestNamedTensor::test_binary_ops, test/test_namedtensor.py::TestNamedTensor::test_bitwise_not, test/test_namedtensor.py::TestNamedTensor::test_bmm, test/test_namedtensor.py::TestNamedTensor::test_cat, test/test_namedtensor.py::TestNamedTensor::test_cdist, test/test_namedtensor.py::TestNamedTensor::test_comparison_ops, test/test_namedtensor.py::TestNamedTensor::test_copy_transpose, test/test_namedtensor.py::TestNamedTensor::test_cummax_cummin, test/test_namedtensor.py::TestNamedTensor::test_detach, test/test_namedtensor.py::TestNamedTensor::test_diagonal, test/test_namedtensor.py::TestNamedTensor::test_dot, test/test_namedtensor.py::TestNamedTensor::test_equal, test/test_namedtensor.py::TestNamedTensor::test_expand, test/test_namedtensor.py::TestNamedTensor::test_factory_coverage, test/test_namedtensor.py::TestNamedTensor::test_factory_edge_cases, test/test_namedtensor.py::TestNamedTensor::test_flatten, test/test_namedtensor.py::TestNamedTensor::test_flatten_index_error, test/test_namedtensor.py::TestNamedTensor::test_flatten_nodims, test/test_namedtensor.py::TestNamedTensor::test_has_names, test/test_namedtensor.py::TestNamedTensor::test_index_fill, test/test_namedtensor.py::TestNamedTensor::test_info_smoke, test/test_namedtensor.py::TestNamedTensor::test_logcumsumexp, test/test_namedtensor.py::TestNamedTensor::test_logical_not, test/test_namedtensor.py::TestNamedTensor::test_logical_ops, test/test_namedtensor.py::TestNamedTensor::test_masked_fill, test/test_namedtensor.py::TestNamedTensor::test_masked_select, test/test_namedtensor.py::TestNamedTensor::test_matmul, test/test_namedtensor.py::TestNamedTensor::test_max_pooling, test/test_namedtensor.py::TestNamedTensor::test_max_pooling_without_names_does_not_warn, test/test_namedtensor.py::TestNamedTensor::test_mm, test/test_namedtensor.py::TestNamedTensor::test_mv, test/test_namedtensor.py::TestNamedTensor::test_no_jit_script_support, test/test_namedtensor.py::TestNamedTensor::test_no_jit_tracer_support, test/test_namedtensor.py::TestNamedTensor::test_no_multiprocessing_support, test/test_namedtensor.py::TestNamedTensor::test_no_pickle_support, test/test_namedtensor.py::TestNamedTensor::test_no_save_support, test/test_namedtensor.py::TestNamedTensor::test_noncontig_contiguous, test/test_namedtensor.py::TestNamedTensor::test_none_names_refcount, test/test_namedtensor.py::TestNamedTensor::test_nyi_dimname_overload_msg, test/test_namedtensor.py::TestNamedTensor::test_out_fn_semantics, test/test_namedtensor.py::TestNamedTensor::test_pow_special, test/test_namedtensor.py::TestNamedTensor::test_py3_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_reduction_fns, test/test_namedtensor.py::TestNamedTensor::test_refine_names, test/test_namedtensor.py::TestNamedTensor::test_rename, test/test_namedtensor.py::TestNamedTensor::test_rename_, test/test_namedtensor.py::TestNamedTensor::test_rename_globber, test/test_namedtensor.py::TestNamedTensor::test_rename_rename_map, test/test_namedtensor.py::TestNamedTensor::test_repr, test/test_namedtensor.py::TestNamedTensor::test_resize, test/test_namedtensor.py::TestNamedTensor::test_select, test/test_namedtensor.py::TestNamedTensor::test_select_cuda, test/test_namedtensor.py::TestNamedTensor::test_set_names_property, test/test_namedtensor.py::TestNamedTensor::test_size, test/test_namedtensor.py::TestNamedTensor::test_split_fns_propagates_names, test/test_namedtensor.py::TestNamedTensor::test_squeeze, test/test_namedtensor.py::TestNamedTensor::test_stride, test/test_namedtensor.py::TestNamedTensor::test_support_device_named_grad, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_lists, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_named_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_numpy, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_grad_is_unnamed, test/test_namedtensor.py::TestNamedTensor::test_transpose_variants, test/test_namedtensor.py::TestNamedTensor::test_trivial, test/test_namedtensor.py::TestNamedTensor::test_unary_propagate_names_fns, test/test_namedtensor.py::TestNamedTensor::test_unflatten, test/test_namedtensor.py::TestNamedTensor::test_unsupported_op_error_msg, test/test_namedtensor.py::TestNamedTensor::test_using_seen_interned_string_doesnt_bump_refcount, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_interned_string_bumps_refcount_permanently, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_uninterned_string_refcounts
2025-03-14T05:26:26.8273448Z 
2025-03-14T05:26:30.4084140Z Running dynamo/test_optimizers 1/1 ... [2025-03-14 05:26:30.407986]
2025-03-14T05:26:30.4085043Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:26:30.4087413Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:26:30.408354]
2025-03-14T05:26:35.1794442Z 
2025-03-14T05:26:35.1795372Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_58d4fc72d1728b2e_.log
2025-03-14T05:26:35.1797087Z Running 3 items in this shard: test/dynamo/test_optimizers.py::End2EndTests::test_init_group, test/dynamo/test_optimizers.py::End2EndTests::test_optimizing_over_tensor_with_requires_grad, test/dynamo/test_optimizers.py::End2EndTests::test_state_dict
2025-03-14T05:26:35.1798175Z 
2025-03-14T05:26:38.8216998Z Running inductor/test_cuda_repro 1/1 ... [2025-03-14 05:26:38.821284]
2025-03-14T05:26:38.8217763Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:26:38.8219888Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:26:38.821655]
2025-03-14T05:26:45.9233629Z 
2025-03-14T05:26:45.9234619Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_1fe7afee92094406_.log
2025-03-14T05:26:45.9235342Z 
2025-03-14T05:26:49.6631894Z Running inductor/test_minifier 1/1 ... [2025-03-14 05:26:49.662763]
2025-03-14T05:26:49.6632807Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:26:49.6634933Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:26:49.663153]
2025-03-14T05:28:27.5796885Z 
2025-03-14T05:28:27.5798270Z inductor/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_1.1_985fbc156b166af3_.log
2025-03-14T05:28:27.5807103Z Running 14 items in this shard: test/inductor/test_minifier.py::MinifierTests::test_accuracy_vs_strict_accuracy, test/inductor/test_minifier.py::MinifierTests::test_after_aot_cpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_after_aot_cpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_after_aot_gpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_after_aot_gpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_cpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_cpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_cpu_compile_error_unflatten, test/inductor/test_minifier.py::MinifierTests::test_aoti_gpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_gpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_gpu_compile_error_unflatten, test/inductor/test_minifier.py::MinifierTests::test_constant_in_graph, test/inductor/test_minifier.py::MinifierTests::test_offload_to_disk, test/inductor/test_minifier.py::MinifierTests::test_rmse_improves_over_atol
2025-03-14T05:28:27.5814968Z 
2025-03-14T05:28:31.1684771Z Running dynamo/test_modes 1/1 ... [2025-03-14 05:28:31.168044]
2025-03-14T05:28:31.1685564Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:28:31.1687444Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:28:31.168373]
2025-03-14T05:30:20.8511655Z 
2025-03-14T05:30:20.8513048Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_69a97cd2a686998a_.log
2025-03-14T05:30:20.8528112Z Running 23 items in this shard: test/dynamo/test_modes.py::TorchDispatchModeTests::test_skip_torch_dispatch_modes, test/dynamo/test_modes.py::TorchFunctionModeTests::test_builtin_equivalent_funcs, test/dynamo/test_modes.py::TorchFunctionModeTests::test_error_empty_stack_pop_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_expand, test/dynamo/test_modes.py::TorchFunctionModeTests::test_flex_attention, test/dynamo/test_modes.py::TorchFunctionModeTests::test_intermedate_torch_function_mode_construction_mutation, test/dynamo/test_modes.py::TorchFunctionModeTests::test_len_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_nested_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_pop_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_push_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_register_hook, test/dynamo/test_modes.py::TorchFunctionModeTests::test_stack_state_clear_default_device, test/dynamo/test_modes.py::TorchFunctionModeTests::test_stack_state_mutation_default_device, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_and_pop_graph_break, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_and_pop_graph_break_mutation, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_disable, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_enabled_guard, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_enter_exit, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_graph_break, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_guards_cpp, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_guards_py, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_highest_priority, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_restore_on_exc
2025-03-14T05:30:20.8545504Z 
2025-03-14T05:30:21.4384351Z Uploading artifacts took 0.59 seconds
2025-03-14T05:30:24.5119657Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2025-03-14 05:30:24.511524]
2025-03-14T05:30:24.5120701Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:30:24.5123311Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:30:24.511973]
2025-03-14T05:30:28.1813809Z 
2025-03-14T05:30:28.1815021Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_c497897b0616772a_.log
2025-03-14T05:30:28.1817175Z Running 3 items in this shard: test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_gh_15395, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init2
2025-03-14T05:30:28.1818429Z 
2025-03-14T05:30:31.8005882Z Running dynamo/test_decorators 1/1 ... [2025-03-14 05:30:31.800228]
2025-03-14T05:30:31.8006418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:30:31.8008306Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_decorators.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:30:31.800584]
2025-03-14T05:30:44.3320711Z 
2025-03-14T05:30:44.3322028Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_bee72e251dfb39ab_.log
2025-03-14T05:30:44.3342019Z Running 51 items in this shard: test/dynamo/test_decorators.py::DecoratorTests::test_allow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_allow_in_graph_no_id_reuse, test/dynamo/test_decorators.py::DecoratorTests::test_assume_constant_result_on_computation_with_graph_input, test/dynamo/test_decorators.py::DecoratorTests::test_assume_constant_result_on_user_defined_fn, test/dynamo/test_decorators.py::DecoratorTests::test_class_methods, test/dynamo/test_decorators.py::DecoratorTests::test_disable_for_custom_op, test/dynamo/test_decorators.py::DecoratorTests::test_disable_ignores_outer_wraps, test/dynamo/test_decorators.py::DecoratorTests::test_disable_nn_module_with_class_decorator, test/dynamo/test_decorators.py::DecoratorTests::test_disable_nn_modules_forward_hook, test/dynamo/test_decorators.py::DecoratorTests::test_disable_optimize, test/dynamo/test_decorators.py::DecoratorTests::test_disallow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_incorrect_usage_disallow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_address_guarded, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_address_unguarded, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_nn_module, test/dynamo/test_decorators.py::DecoratorTests::test_nested_disable_decorator, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_newly_constructed_trace_register_constant_type_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_captured_external_tensor, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_custom_class_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function_kwarg, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_nested_custom_class, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_nested_custom_class_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_newly_constructed_custom_class_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_newly_constructed_dict_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_no_action_at_a_distance, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_object_in_context_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_on_method, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_custom_class, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_custom_class_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_dict, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_dict_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_register_constant_type_guard, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_tensor_args, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_tuple_and_sym_int_output, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_aot_eager_then_compile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_on_recompile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_then_compile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_then_compile_with_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_fail_on_recompile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_fail_on_recompile_with_disable, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_forbid_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_backend, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_backend_with_disable, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_eager, test/dynamo/test_decorators.py::DecoratorTests::test_skip, test/dynamo/test_decorators.py::DecoratorTests::test_substitute_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_torch_guards_stack_frame_register_inlining_disable, test/dynamo/test_decorators.py::DecoratorTests::test_torch_guards_stack_frame_register_inlining_partially_disable
2025-03-14T05:30:44.3361292Z 
2025-03-14T05:30:47.8739113Z Running test_functionalization 1/1 ... [2025-03-14 05:30:47.873492]
2025-03-14T05:30:47.8739687Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:30:47.8740847Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_functionalization.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:30:47.873828]
2025-03-14T05:30:53.4873174Z 
2025-03-14T05:30:53.4874872Z inductor/test_torchinductor_opinfo 33/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_33.42_aea4eca34fec84a2_.log
2025-03-14T05:30:53.4958915Z Running 88 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rxor___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__batch_norm_with_update_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__batch_norm_with_update_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_and_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_xor_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_column_stack_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_constant_pad_nd_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_copysign_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cos_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cross_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumprod_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_floor_rounding_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eye_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfftn_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_like_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_inner_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_2inputs_2outputs_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logdet_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mH_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mH_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_fill_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_logsumexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_minimum_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nan_to_num_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nan_to_num_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool1d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv_transpose2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardtanh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_layer_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_leaky_relu_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_linear_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool2d_grad_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_rms_norm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_threshold_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_fro_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_inf_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_in_place_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ravel_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_conj_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_round_decimals_0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsub_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_mean_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sigmoid_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signbit_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinh_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_softmax_with_dtype_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_v_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_hermite_polynomial_h_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i0e_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i1_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_multiple_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensor_split_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trace_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unflatten_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_uniform_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unravel_index_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_var_mean_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_cpu_float16
2025-03-14T05:30:53.5041261Z 
2025-03-14T05:30:56.1503993Z 
2025-03-14T05:30:56.1511899Z test_functionalization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functionalization_1.1_031eebbaf33bdf68_.log
2025-03-14T05:30:56.1594363Z Running 112 items in this shard: test/test_functionalization.py::TestFunctionalization::test_advanced_indexing, test/test_functionalization.py::TestFunctionalization::test_advanced_indexing_correct_strides, test/test_functionalization.py::TestFunctionalization::test_aliases_maintained_after_pass_when_reapplying_views, test/test_functionalization.py::TestFunctionalization::test_as_strided, test/test_functionalization.py::TestFunctionalization::test_batch_norm, test/test_functionalization.py::TestFunctionalization::test_cat, test/test_functionalization.py::TestFunctionalization::test_channels_last_contiguous, test/test_functionalization.py::TestFunctionalization::test_copy_, test/test_functionalization.py::TestFunctionalization::test_copy_stride_mismatch, test/test_functionalization.py::TestFunctionalization::test_diagonal, test/test_functionalization.py::TestFunctionalization::test_diagonal_mutated_input, test/test_functionalization.py::TestFunctionalization::test_everything, test/test_functionalization.py::TestFunctionalization::test_expand_symint, test/test_functionalization.py::TestFunctionalization::test_fill_, test/test_functionalization.py::TestFunctionalization::test_freeze, test/test_functionalization.py::TestFunctionalization::test_index_mutation_on_non_input, test/test_functionalization.py::TestFunctionalization::test_inplace_on_non_view, test/test_functionalization.py::TestFunctionalization::test_instance_norm, test/test_functionalization.py::TestFunctionalization::test_metadata_change, test/test_functionalization.py::TestFunctionalization::test_metadata_change_out_op, test/test_functionalization.py::TestFunctionalization::test_mixed_wrappers_invalid, test/test_functionalization.py::TestFunctionalization::test_mixed_wrappers_valid, test/test_functionalization.py::TestFunctionalization::test_multi_out, test/test_functionalization.py::TestFunctionalization::test_multiple_views_of_same_base, test/test_functionalization.py::TestFunctionalization::test_mutable_op_not_inplace_or_other, test/test_functionalization.py::TestFunctionalization::test_mutation_overlapping_mem, test/test_functionalization.py::TestFunctionalization::test_nested_functions_propagate_updates, test/test_functionalization.py::TestFunctionalization::test_only_one_view, test/test_functionalization.py::TestFunctionalization::test_optional_tensor_list, test/test_functionalization.py::TestFunctionalization::test_python_functionalization, test/test_functionalization.py::TestFunctionalization::test_python_functionalization_conj, test/test_functionalization.py::TestFunctionalization::test_python_functionalization_is_conj, test/test_functionalization.py::TestFunctionalization::test_python_functionalization_is_neg, test/test_functionalization.py::TestFunctionalization::test_python_functionalization_lift_fresh, test/test_functionalization.py::TestFunctionalization::test_python_functionalization_lift_fresh_storage, test/test_functionalization.py::TestFunctionalization::test_python_functionalization_neg, test/test_functionalization.py::TestFunctionalization::test_python_functionalization_zero_tensor, test/test_functionalization.py::TestFunctionalization::test_reapply_views_simple, test/test_functionalization.py::TestFunctionalization::test_resize_larger_invalid, test/test_functionalization.py::TestFunctionalization::test_resize_larger_valid, test/test_functionalization.py::TestFunctionalization::test_resize_same_size_diff_rank, test/test_functionalization.py::TestFunctionalization::test_resize_smaller, test/test_functionalization.py::TestFunctionalization::test_save_for_backwards_segfault, test/test_functionalization.py::TestFunctionalization::test_scalars, test/test_functionalization.py::TestFunctionalization::test_set_, test/test_functionalization.py::TestFunctionalization::test_simple, test/test_functionalization.py::TestFunctionalization::test_simple_out, test/test_functionalization.py::TestFunctionalization::test_slice, test/test_functionalization.py::TestFunctionalization::test_split, test/test_functionalization.py::TestFunctionalization::test_split_with_sizes, test/test_functionalization.py::TestFunctionalization::test_tensor_ctr, test/test_functionalization.py::TestFunctionalization::test_tensor_list_composite, test/test_functionalization.py::TestFunctionalization::test_tensor_list_mixed_functional_nonfunctional, test/test_functionalization.py::TestFunctionalization::test_unbind, test/test_functionalization.py::TestFunctionalization::test_view_clone_view_inplace, test/test_functionalization.py::TestFunctionalization::test_view_inplace, test/test_functionalization.py::TestCrossRefFunctionalization::test_advanced_indexing, test/test_functionalization.py::TestCrossRefFunctionalization::test_advanced_indexing_correct_strides, test/test_functionalization.py::TestCrossRefFunctionalization::test_aliases_maintained_after_pass_when_reapplying_views, test/test_functionalization.py::TestCrossRefFunctionalization::test_as_strided, test/test_functionalization.py::TestCrossRefFunctionalization::test_batch_norm, test/test_functionalization.py::TestCrossRefFunctionalization::test_cat, test/test_functionalization.py::TestCrossRefFunctionalization::test_channels_last_contiguous, test/test_functionalization.py::TestCrossRefFunctionalization::test_copy_, test/test_functionalization.py::TestCrossRefFunctionalization::test_copy_stride_mismatch, test/test_functionalization.py::TestCrossRefFunctionalization::test_diagonal, test/test_functionalization.py::TestCrossRefFunctionalization::test_diagonal_mutated_input, test/test_functionalization.py::TestCrossRefFunctionalization::test_everything, test/test_functionalization.py::TestCrossRefFunctionalization::test_expand_symint, test/test_functionalization.py::TestCrossRefFunctionalization::test_fill_, test/test_functionalization.py::TestCrossRefFunctionalization::test_freeze, test/test_functionalization.py::TestCrossRefFunctionalization::test_index_mutation_on_non_input, test/test_functionalization.py::TestCrossRefFunctionalization::test_inplace_on_non_view, test/test_functionalization.py::TestCrossRefFunctionalization::test_instance_norm, test/test_functionalization.py::TestCrossRefFunctionalization::test_metadata_change, test/test_functionalization.py::TestCrossRefFunctionalization::test_metadata_change_out_op, test/test_functionalization.py::TestCrossRefFunctionalization::test_mixed_wrappers_invalid, test/test_functionalization.py::TestCrossRefFunctionalization::test_mixed_wrappers_valid, test/test_functionalization.py::TestCrossRefFunctionalization::test_multi_out, test/test_functionalization.py::TestCrossRefFunctionalization::test_multiple_views_of_same_base, test/test_functionalization.py::TestCrossRefFunctionalization::test_mutable_op_not_inplace_or_other, test/test_functionalization.py::TestCrossRefFunctionalization::test_mutation_overlapping_mem, test/test_functionalization.py::TestCrossRefFunctionalization::test_nested_functions_propagate_updates, test/test_functionalization.py::TestCrossRefFunctionalization::test_only_one_view, test/test_functionalization.py::TestCrossRefFunctionalization::test_optional_tensor_list, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization_conj, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization_is_conj, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization_is_neg, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization_lift_fresh, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization_lift_fresh_storage, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization_neg, test/test_functionalization.py::TestCrossRefFunctionalization::test_python_functionalization_zero_tensor, test/test_functionalization.py::TestCrossRefFunctionalization::test_reapply_views_simple, test/test_functionalization.py::TestCrossRefFunctionalization::test_resize_larger_invalid, test/test_functionalization.py::TestCrossRefFunctionalization::test_resize_larger_valid, test/test_functionalization.py::TestCrossRefFunctionalization::test_resize_same_size_diff_rank, test/test_functionalization.py::TestCrossRefFunctionalization::test_resize_smaller, test/test_functionalization.py::TestCrossRefFunctionalization::test_save_for_backwards_segfault, test/test_functionalization.py::TestCrossRefFunctionalization::test_scalars, test/test_functionalization.py::TestCrossRefFunctionalization::test_set_, test/test_functionalization.py::TestCrossRefFunctionalization::test_simple, test/test_functionalization.py::TestCrossRefFunctionalization::test_simple_out, test/test_functionalization.py::TestCrossRefFunctionalization::test_slice, test/test_functionalization.py::TestCrossRefFunctionalization::test_split, test/test_functionalization.py::TestCrossRefFunctionalization::test_split_with_sizes, test/test_functionalization.py::TestCrossRefFunctionalization::test_tensor_ctr, test/test_functionalization.py::TestCrossRefFunctionalization::test_tensor_list_composite, test/test_functionalization.py::TestCrossRefFunctionalization::test_tensor_list_mixed_functional_nonfunctional, test/test_functionalization.py::TestCrossRefFunctionalization::test_unbind, test/test_functionalization.py::TestCrossRefFunctionalization::test_view_clone_view_inplace, test/test_functionalization.py::TestCrossRefFunctionalization::test_view_inplace
2025-03-14T05:30:56.1673517Z 
2025-03-14T05:30:57.2763541Z Running inductor/test_triton_kernels 1/1 ... [2025-03-14 05:30:57.275973]
2025-03-14T05:30:57.2764521Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:30:57.2766671Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_triton_kernels.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:30:57.276317]
2025-03-14T05:30:59.7198175Z Running nn/test_multihead_attention 1/1 ... [2025-03-14 05:30:59.719465]
2025-03-14T05:30:59.7198725Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:30:59.7200839Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_multihead_attention.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:30:59.719847]
2025-03-14T05:31:05.7546274Z 
2025-03-14T05:31:05.7548028Z inductor/test_triton_kernels 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_kernels_1.1_3c9b3e64477c3bc8_.log
2025-03-14T05:31:05.7684804Z Running 280 items in this shard: test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_False_autotune_False, test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_False_autotune_True, test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_True_autotune_False, test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_True_autotune_True, test/inductor/test_triton_kernels.py::KernelTests::test_i64_input, test/inductor/test_triton_kernels.py::KernelTests::test_layout_constraint_needs_fixed_stride_order, test/inductor/test_triton_kernels.py::KernelTests::test_no_nan_kernels, test/inductor/test_triton_kernels.py::KernelTests::test_tma_capture_and_functionalize_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_tma_capture_and_functionalize_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_dedup, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_False_after_create_desc_False, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_False_after_create_desc_True, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_True_after_create_desc_False, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_True_after_create_desc_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_attrs_dict_equal_1_None_format, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_inductor_grid_type_2, 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test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_different_shapes_size_16_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_different_shapes_size_4_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_different_shapes_size_4_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_dtype_view_cfg_cpp_wrapper, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_dtype_view_cfg_normal, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_empty_autotune_config_dict_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_empty_autotune_config_dict_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_empty_autotune_config_dict_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_equal_to_1_arg_dynamic_False, 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2025-03-14T05:31:05.7814801Z 
2025-03-14T05:31:07.5965089Z 
2025-03-14T05:31:07.5966188Z nn/test_multihead_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_multihead_attention_1.1_9d0cf89435d406ae_.log
2025-03-14T05:31:07.5976947Z Running 19 items in this shard: test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attention_average_attn_weights_False, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attention_average_attn_weights_True, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_3d_attn_mask, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_fast_path_invalid_shape, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_invalid_shape, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_nested_tensor_outside_fast_path, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_no_bias, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float16, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float32, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float16, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float32, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_fast_path_query_and_bias_have_different_dtypes_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_fast_path_small_test_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_in_proj_bias_none_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_in_proj_weight_none_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_self_attn_two_masks_fast_path_cpu, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_self_attn_two_masks_fast_path_mock_cpu
2025-03-14T05:31:07.5987126Z 
2025-03-14T05:31:09.6707461Z Running test_stateless 1/1 ... [2025-03-14 05:31:09.670349]
2025-03-14T05:31:09.6707959Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:09.6709681Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_stateless.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:09.670708]
2025-03-14T05:31:11.1577375Z Running export/test_db 1/1 ... [2025-03-14 05:31:11.157359]
2025-03-14T05:31:11.1577847Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:11.1579140Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_db.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:11.157685]
2025-03-14T05:31:16.7800747Z 
2025-03-14T05:31:16.7802000Z export/test_db 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_db_1.1_41330b9647cf047b_.log
2025-03-14T05:31:16.7827409Z Running 36 items in this shard: test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_dynamic_shape_round, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_model_attr_mutation, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_optional_input, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_unsupported_operator, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_assume_constant_result, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_autograd_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_class_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_class_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_nested_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_nonlocal_variables, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_closed_over_variable, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_operands, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_predicate, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_constrain_as_size_example, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_constrain_as_value_example, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_decorator, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dictionary, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_assert, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_constructor, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_if_guard, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_map, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_slicing, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_view, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_fn_with_kwargs, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_list_contains, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_list_unpack, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_nested_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_null_context_manager, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_pytree_flatten, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_scalar_output, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_specialized_attribute, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_static_for_loop, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_static_if, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_tensor_setattr, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_type_reflection_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_user_input_mutation
2025-03-14T05:31:16.7852587Z 
2025-03-14T05:31:18.7480933Z 
2025-03-14T05:31:18.7482052Z test_stateless 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_stateless_1.1_6a982ab1f7e73c04_.log
2025-03-14T05:31:18.7502952Z Running 50 items in this shard: test/test_stateless.py::TestStatelessFunctionalAPI::test_circular_references_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_circular_references_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_batch_norm_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_batch_norm_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_member_reference_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_member_reference_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_multiple_dicts_error, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_tuple_dicts, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_error_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_error_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_gradient_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_gradient_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_jit_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_jit_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_kwargs_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_kwargs_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_in_place_operator_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_in_place_operator_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_module_fail_reset_to_original_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_module_fail_reset_to_original_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_some_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_some_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_special_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_special_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_some_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_some_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrized_module_change_parametrization_original_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrized_module_change_parametrization_original_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_errors_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_errors_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_no_error_without_flag, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_warns_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_warns_torch_func, test/test_stateless.py::TestStatelessDeprecation::test_private_stateless_warns, test/test_stateless.py::TestStatelessDeprecation::test_stateless_functional_call_warns, test/test_stateless.py::TestPythonOptimizeMode::test_runs_with_optimize_flag
2025-03-14T05:31:18.7523362Z 
2025-03-14T05:31:20.3917690Z Running inductor/test_perf 1/1 ... [2025-03-14 05:31:20.391391]
2025-03-14T05:31:20.3918186Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:20.3919743Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_perf.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:20.391744]
2025-03-14T05:31:22.3694641Z Running test_content_store 1/1 ... [2025-03-14 05:31:22.369112]
2025-03-14T05:31:22.3695259Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:22.3696646Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_content_store.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:22.369429]
2025-03-14T05:31:27.5256469Z 
2025-03-14T05:31:27.5257633Z inductor/test_perf 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_perf_1.1_1f28179e4109fa68_.log
2025-03-14T05:31:27.5258322Z 
2025-03-14T05:31:31.1549554Z Running inductor/test_async_compile 1/1 ... [2025-03-14 05:31:31.154588]
2025-03-14T05:31:31.1550093Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:31.1552083Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_async_compile.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:31.154951]
2025-03-14T05:31:32.4987064Z 
2025-03-14T05:31:32.4988441Z test_content_store 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_content_store_1.1_d370feab12bdcd8a_.log
2025-03-14T05:31:32.4991802Z Running 4 items in this shard: test/test_content_store.py::TestContentStoreCPU::test_basic_cpu, test/test_content_store.py::TestContentStoreCPU::test_load_tensor_cpu, test/test_content_store.py::TestContentStoreCPU::test_repeated_hash_cpu, test/test_content_store.py::TestContentStoreCPU::test_scalar_cpu
2025-03-14T05:31:32.4994151Z 
2025-03-14T05:31:36.1283176Z Running dynamo/test_autograd_function 1/1 ... [2025-03-14 05:31:36.127935]
2025-03-14T05:31:36.1284065Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:36.1286276Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:36.128316]
2025-03-14T05:31:38.5795293Z 
2025-03-14T05:31:38.5796565Z inductor/test_async_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_async_compile_1.1_a881d21b712a1c33_.log
2025-03-14T05:31:38.5798466Z Running 3 items in this shard: test/inductor/test_async_compile.py::TestAsyncCompile::test_pool_method_fork, test/inductor/test_async_compile.py::TestAsyncCompile::test_pool_method_spawn, test/inductor/test_async_compile.py::TestAsyncCompile::test_pool_method_subprocess
2025-03-14T05:31:38.5799721Z 
2025-03-14T05:31:42.2101014Z Running test_expanded_weights 1/1 ... [2025-03-14 05:31:42.209747]
2025-03-14T05:31:42.2101602Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:42.2103143Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_expanded_weights.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:42.210080]
2025-03-14T05:31:46.7585847Z 
2025-03-14T05:31:46.7587246Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_8fd6a2a4ff2794a5_.log
2025-03-14T05:31:46.7603871Z Running 40 items in this shard: test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_allow_in_graph, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_amp_custom_fwd_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_assert_is_contiguous_after_matmul, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_assert_is_contiguous_on_grad_output_directly, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_equivalence, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_has_graph_break, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_backward_returns_none_for_tensor_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_classmethod, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_data_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_default_values, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_enum_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_forward_returns_constant, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_mark_and_save, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_save_and_mark, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_with_bound_free_variable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_fwd_no_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_fwd_propogation_correctness, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_linear_setup_context, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_mark_multi_output_non_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_mark_non_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_materialize_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multi_output, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multiple_different_non_tensor_inputs, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_needs_input_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_once_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_print_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_repeated_save_for_backward_calls, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_requires_grad_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_save_for_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_set_materialize_grads_no_graph_break, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smoke_from_test_autograd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_symint_issue_111031, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_tensor_and_complex_structures, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_stride_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_list_as_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_subclass_intermediary_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_basic, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_multiple_out, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tuple_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_user_defined_object_as_input
2025-03-14T05:31:46.7619825Z 
2025-03-14T05:31:50.7459254Z Running inductor/test_custom_post_grad_passes 1/1 ... [2025-03-14 05:31:50.745509]
2025-03-14T05:31:50.7460306Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:50.7462564Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_custom_post_grad_passes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:50.745864]
2025-03-14T05:31:55.8463664Z 
2025-03-14T05:31:55.8464899Z test_expanded_weights 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_expanded_weights_1.1_fcdae7b8bb343b45_.log
2025-03-14T05:31:55.8558208Z Running 189 items in this shard: test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_forward_helper_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_forward_helper_failure_args_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_set_grad_sample_if_exists_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_set_grad_sample_if_exists_failure_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_sum_over_all_but_batch_and_last_n_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_unpack_expanded_weight_or_tensor_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_unpack_expanded_weight_or_tensor_failure_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_unpack_expanded_weight_or_tensor_with_custom_function_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_cnn_model_mean_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_cnn_model_sum_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_embedding_model_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_error_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_float16, 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test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_per_sample_api_failing_cpu
2025-03-14T05:31:55.8649362Z 
2025-03-14T05:31:59.4694586Z Running torch_np/test_basic 1/1 ... [2025-03-14 05:31:59.469129]
2025-03-14T05:31:59.4695071Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:31:59.4697563Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_basic.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:31:59.469493]
2025-03-14T05:32:04.3412097Z 
2025-03-14T05:32:04.3413449Z torch_np/test_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_basic_1.1_1fad76d51614a554_.log
2025-03-14T05:32:04.3546126Z Running 453 items in this shard: test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func2, 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test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func2_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func2_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func3_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func3_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func3_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func3_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func4_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func4_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func4_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func4_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func5_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func5_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func5_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func5_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func6_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func6_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func6_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func6_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func7_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func7_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func7_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func7_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func8_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func8_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func8_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func8_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func9_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func9_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func9_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_list_func9_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func0_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func0_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func0_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func0_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func10_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func10_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func10_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func10_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func1_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func1_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func1_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func1_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func2_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func2_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func2_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func2_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func3_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func3_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func3_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func3_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func4_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func4_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func4_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func4_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_array_func0_axes0, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_array_func0_axes1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_array_func0_axes2, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_list_func0_axes0, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_list_func0_axes1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_list_func0_axes2, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_tensor_func0_axes0, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_tensor_func0_axes1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_tensor_func0_axes2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func0, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func1, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func3, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func4, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func0, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func1, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func3, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func4, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func0, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func1, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func3, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func4, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func2_np_func2, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func2_np_func2, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func2_np_func2, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func0, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func1, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func2, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func0, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func1, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func2, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func4, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func5, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func6, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func0_args0, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func1_args1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func2_args2, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func3_args3, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func4_args4, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func5_args5, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func6_args6, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func7_args7, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func8_args8, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func9_args9, test/torch_np/test_basic.py::TestNormalizations::test_too_few_args_positional, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args_with_defaults, test/torch_np/test_basic.py::TestCopyTo::test_copyto_basic, test/torch_np/test_basic.py::TestCopyTo::test_copyto_typecast, test/torch_np/test_basic.py::TestCopyTo::test_copytobcast, test/torch_np/test_basic.py::TestDivmod::test_divmod_no_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_both_pos_and_kw, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_list, test/torch_np/test_basic.py::TestDivmod::test_divmod_pos_only, test/torch_np/test_basic.py::TestSmokeNotImpl::test_nimpl_basic, test/torch_np/test_basic.py::TestDefaultDtype::test_defaultdtype_defaults, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_float32, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_pytorch, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_float32, test/torch_np/test_basic.py::TestExport::test_exported_objects, test/torch_np/test_basic.py::TestCtorNested::test_arrays_in_lists, test/torch_np/test_basic.py::TestMisc::test_f16_on_cuda, test/torch_np/test_basic.py::TestMisc::test_ndarrays_to_tensors
2025-03-14T05:32:04.3677952Z 
2025-03-14T05:32:07.9301908Z Running inductor/test_control_flow 1/1 ... [2025-03-14 05:32:07.929737]
2025-03-14T05:32:07.9302813Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:32:07.9304908Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_control_flow.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:32:07.930126]
2025-03-14T05:32:08.9390134Z 
2025-03-14T05:32:08.9391216Z inductor/test_custom_post_grad_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_custom_post_grad_passes_1.1_dc259a8724be4365_.log
2025-03-14T05:32:08.9394316Z Running 5 items in this shard: test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_joint_pass_post, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_joint_pass_pre, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_post_pass, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_pre_grad_pass, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_pre_pass
2025-03-14T05:32:08.9396884Z 
2025-03-14T05:32:12.5062830Z Running inductor/test_ordered_set 1/1 ... [2025-03-14 05:32:12.505919]
2025-03-14T05:32:12.5063336Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:32:12.5065723Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_ordered_set.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:32:12.506318]
2025-03-14T05:32:16.0563578Z 
2025-03-14T05:32:16.0564843Z inductor/test_control_flow 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_control_flow_1.1_71f9dcb45c07fc50_.log
2025-03-14T05:32:16.0611094Z Running 105 items in this shard: test/inductor/test_control_flow.py::CondTests::test_cond_advanced_dynamic_shapes_device_cpu, test/inductor/test_control_flow.py::CondTests::test_cond_advanced_dynamic_shapes_device_cuda, test/inductor/test_control_flow.py::CondTests::test_cond_aliasing_outputs, test/inductor/test_control_flow.py::CondTests::test_cond_control_flow_with_precomputed_size, test/inductor/test_control_flow.py::CondTests::test_cond_decompose_ops_in_subgraph_device_cpu, test/inductor/test_control_flow.py::CondTests::test_cond_decompose_ops_in_subgraph_device_cuda, test/inductor/test_control_flow.py::CondTests::test_cond_decompose_ops_in_subgraph_recursive_device_cpu, test/inductor/test_control_flow.py::CondTests::test_cond_decompose_ops_in_subgraph_recursive_device_cuda, test/inductor/test_control_flow.py::CondTests::test_cond_inductor_fx_passes_recursively_applied, test/inductor/test_control_flow.py::CondTests::test_cond_mismatched_branch_output_size_device_cpu_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_mismatched_branch_output_size_device_cpu_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_mismatched_branch_output_size_device_cuda_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_mismatched_branch_output_size_device_cuda_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_multiple_outputs_device_cpu_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_multiple_outputs_device_cpu_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_multiple_outputs_device_cuda_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_multiple_outputs_device_cuda_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_nested_control_flow_device_cpu_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_nested_control_flow_device_cpu_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_nested_control_flow_device_cuda_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_nested_control_flow_device_cuda_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_non_tensor_predicates_device_cpu_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_non_tensor_predicates_device_cpu_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_non_tensor_predicates_device_cuda_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_non_tensor_predicates_device_cuda_dynamic_True, test/inductor/test_control_flow.py::CondTests::test_cond_outer_code_before_after_device_cpu_dynamic_False, test/inductor/test_control_flow.py::CondTests::test_cond_outer_code_before_after_device_cpu_dynamic_True, 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test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_nested_control_flow_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_nested_control_flow_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_nested_control_flow_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_simple_control_flow_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_simple_control_flow_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_simple_control_flow_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_simple_control_flow_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_conv_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_conv_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_conv_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_conv_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_data_dependent_in_out_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_data_dependent_in_out_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_data_dependent_in_out_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_data_dependent_in_out_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_data_dependent_in_out_mismatch_dynamic_False, 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test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_outer_code_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_outer_code_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_outer_code_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_parameters_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_parameters_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_parameters_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_parameters_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_pytree_inputs_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_pytree_inputs_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_pytree_inputs_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_pytree_inputs_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_sym_expr_cond_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_sym_expr_cond_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_sym_expr_cond_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_sym_expr_cond_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_unbacked_symint_closure_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_unbacked_symint_closure_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_unbacked_symint_closure_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_with_unbacked_symint_closure_device_cuda_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_zero_loop_device_cpu_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_zero_loop_device_cpu_dynamic_True, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_zero_loop_device_cuda_dynamic_False, test/inductor/test_control_flow.py::WhileLoopTests::test_while_loop_zero_loop_device_cuda_dynamic_True, test/inductor/test_control_flow.py::AssociativeScanTests::test_associative_scan_CUDA_flip_combine_mode_generic_backend_inductor_cpu, test/inductor/test_control_flow.py::AssociativeScanTests::test_associative_scan_CUDA_flip_combine_mode_generic_backend_inductor_device_cuda, test/inductor/test_control_flow.py::AssociativeScanTests::test_associative_scan_CUDA_flip_combine_mode_pointwise_backend_inductor_cpu, test/inductor/test_control_flow.py::AssociativeScanTests::test_associative_scan_CUDA_flip_combine_mode_pointwise_backend_inductor_device_cuda
2025-03-14T05:32:16.0656797Z 
2025-03-14T05:32:18.6292701Z 
2025-03-14T05:32:18.6293682Z inductor/test_ordered_set 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_ordered_set_1.1_df848a0ee4093142_.log
2025-03-14T05:32:18.6419132Z Running 401 items in this shard: test/inductor/test_ordered_set.py::TestJointOps::test_and, test/inductor/test_ordered_set.py::TestJointOps::test_badcmp, test/inductor/test_ordered_set.py::TestJointOps::test_container_iterator, test/inductor/test_ordered_set.py::TestJointOps::test_contains, test/inductor/test_ordered_set.py::TestJointOps::test_cyclical_repr, test/inductor/test_ordered_set.py::TestJointOps::test_deepcopy, test/inductor/test_ordered_set.py::TestJointOps::test_difference, test/inductor/test_ordered_set.py::TestJointOps::test_do_not_rehash_dict_keys, test/inductor/test_ordered_set.py::TestJointOps::test_equality, test/inductor/test_ordered_set.py::TestJointOps::test_free_after_iterating, test/inductor/test_ordered_set.py::TestJointOps::test_gc, test/inductor/test_ordered_set.py::TestJointOps::test_intersection, test/inductor/test_ordered_set.py::TestJointOps::test_isdisjoint, test/inductor/test_ordered_set.py::TestJointOps::test_iterator_pickling, test/inductor/test_ordered_set.py::TestJointOps::test_len, test/inductor/test_ordered_set.py::TestJointOps::test_new_or_init, test/inductor/test_ordered_set.py::TestJointOps::test_or, test/inductor/test_ordered_set.py::TestJointOps::test_pickling, test/inductor/test_ordered_set.py::TestJointOps::test_setOfFrozensets, test/inductor/test_ordered_set.py::TestJointOps::test_sub, test/inductor/test_ordered_set.py::TestJointOps::test_sub_and_super, test/inductor/test_ordered_set.py::TestJointOps::test_subclass_with_custom_hash, test/inductor/test_ordered_set.py::TestJointOps::test_symmetric_difference, test/inductor/test_ordered_set.py::TestJointOps::test_union, test/inductor/test_ordered_set.py::TestJointOps::test_uniquification, test/inductor/test_ordered_set.py::TestJointOps::test_xor, test/inductor/test_ordered_set.py::TestSet::test_add, test/inductor/test_ordered_set.py::TestSet::test_and, test/inductor/test_ordered_set.py::TestSet::test_badcmp, test/inductor/test_ordered_set.py::TestSet::test_clear, test/inductor/test_ordered_set.py::TestSet::test_constructor_identity, test/inductor/test_ordered_set.py::TestSet::test_container_iterator, test/inductor/test_ordered_set.py::TestSet::test_contains, test/inductor/test_ordered_set.py::TestSet::test_copy, test/inductor/test_ordered_set.py::TestSet::test_cyclical_repr, test/inductor/test_ordered_set.py::TestSet::test_deepcopy, test/inductor/test_ordered_set.py::TestSet::test_difference, test/inductor/test_ordered_set.py::TestSet::test_difference_update, test/inductor/test_ordered_set.py::TestSet::test_discard, test/inductor/test_ordered_set.py::TestSet::test_do_not_rehash_dict_keys, test/inductor/test_ordered_set.py::TestSet::test_equality, test/inductor/test_ordered_set.py::TestSet::test_free_after_iterating, test/inductor/test_ordered_set.py::TestSet::test_gc, test/inductor/test_ordered_set.py::TestSet::test_hash, test/inductor/test_ordered_set.py::TestSet::test_iand, test/inductor/test_ordered_set.py::TestSet::test_init, test/inductor/test_ordered_set.py::TestSet::test_inplace_on_self, test/inductor/test_ordered_set.py::TestSet::test_intersection, test/inductor/test_ordered_set.py::TestSet::test_intersection_update, test/inductor/test_ordered_set.py::TestSet::test_ior, test/inductor/test_ordered_set.py::TestSet::test_isdisjoint, test/inductor/test_ordered_set.py::TestSet::test_isub, test/inductor/test_ordered_set.py::TestSet::test_iterator_pickling, test/inductor/test_ordered_set.py::TestSet::test_ixor, test/inductor/test_ordered_set.py::TestSet::test_len, test/inductor/test_ordered_set.py::TestSet::test_new_or_init, test/inductor/test_ordered_set.py::TestSet::test_or, test/inductor/test_ordered_set.py::TestSet::test_pickling, test/inductor/test_ordered_set.py::TestSet::test_pop, test/inductor/test_ordered_set.py::TestSet::test_remove, test/inductor/test_ordered_set.py::TestSet::test_remove_keyerror_set, test/inductor/test_ordered_set.py::TestSet::test_remove_keyerror_unpacking, test/inductor/test_ordered_set.py::TestSet::test_rich_compare, test/inductor/test_ordered_set.py::TestSet::test_setOfFrozensets, test/inductor/test_ordered_set.py::TestSet::test_set_literal, test/inductor/test_ordered_set.py::TestSet::test_set_literal_evaluation_order, test/inductor/test_ordered_set.py::TestSet::test_set_literal_insertion_order, test/inductor/test_ordered_set.py::TestSet::test_sub, test/inductor/test_ordered_set.py::TestSet::test_sub_and_super, test/inductor/test_ordered_set.py::TestSet::test_subclass_with_custom_hash, test/inductor/test_ordered_set.py::TestSet::test_symmetric_difference, test/inductor/test_ordered_set.py::TestSet::test_symmetric_difference_update, test/inductor/test_ordered_set.py::TestSet::test_union, test/inductor/test_ordered_set.py::TestSet::test_uniquification, test/inductor/test_ordered_set.py::TestSet::test_update, test/inductor/test_ordered_set.py::TestSet::test_weakref, test/inductor/test_ordered_set.py::TestSet::test_xor, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_length, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_in, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_length, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_not_in, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_in, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_length, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_not_in, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_length, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsString::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsString::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsString::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsString::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsString::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsString::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsString::test_length, test/inductor/test_ordered_set.py::TestBasicOpsString::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsString::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsString::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_length, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_length, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_union_empty, test/inductor/test_ordered_set.py::TestExceptionPropagation::test_changingSizeWhileIterating, test/inductor/test_ordered_set.py::TestExceptionPropagation::test_instanceWithException, test/inductor/test_ordered_set.py::TestExceptionPropagation::test_instancesWithoutException, test/inductor/test_ordered_set.py::TestSetOfSets::test_constructor, test/inductor/test_ordered_set.py::TestBinaryOps::test_eq, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_superset, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_superset, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_superset, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_superset, test/inductor/test_ordered_set.py::TestMutate::test_add_absent, test/inductor/test_ordered_set.py::TestMutate::test_add_present, test/inductor/test_ordered_set.py::TestMutate::test_add_until_full, test/inductor/test_ordered_set.py::TestMutate::test_clear, test/inductor/test_ordered_set.py::TestMutate::test_discard_absent, test/inductor/test_ordered_set.py::TestMutate::test_discard_present, test/inductor/test_ordered_set.py::TestMutate::test_pop, test/inductor/test_ordered_set.py::TestMutate::test_remove_absent, test/inductor/test_ordered_set.py::TestMutate::test_remove_present, test/inductor/test_ordered_set.py::TestMutate::test_remove_until_empty, test/inductor/test_ordered_set.py::TestMutate::test_update_empty_tuple, test/inductor/test_ordered_set.py::TestMutate::test_update_unit_tuple_non_overlap, test/inductor/test_ordered_set.py::TestMutate::test_update_unit_tuple_overlap, test/inductor/test_ordered_set.py::TestSubsets::test_issubset, test/inductor/test_ordered_set.py::TestSubsetEqualEmpty::test_issubset, test/inductor/test_ordered_set.py::TestSubsetEqualNonEmpty::test_issubset, test/inductor/test_ordered_set.py::TestSubsetEmptyNonEmpty::test_issubset, test/inductor/test_ordered_set.py::TestSubsetPartial::test_issubset, test/inductor/test_ordered_set.py::TestSubsetNonOverlap::test_issubset, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_union, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_union, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_union, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_union, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsString::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsString::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsString::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsString::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsString::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_union, test/inductor/test_ordered_set.py::TestOnlySetsString::test_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_union, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_update_operator, test/inductor/test_ordered_set.py::TestCopyingEmpty::test_copy, test/inductor/test_ordered_set.py::TestCopyingEmpty::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingSingleton::test_copy, test/inductor/test_ordered_set.py::TestCopyingSingleton::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingTriple::test_copy, test/inductor/test_ordered_set.py::TestCopyingTriple::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingTuple::test_copy, test/inductor/test_ordered_set.py::TestCopyingTuple::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingNested::test_copy, test/inductor/test_ordered_set.py::TestCopyingNested::test_deep_copy, test/inductor/test_ordered_set.py::TestIdentities::test_binopsVsSubsets, test/inductor/test_ordered_set.py::TestIdentities::test_commutativity, test/inductor/test_ordered_set.py::TestIdentities::test_exclusion, test/inductor/test_ordered_set.py::TestIdentities::test_summations, test/inductor/test_ordered_set.py::TestVariousIteratorArgs::test_constructor, test/inductor/test_ordered_set.py::TestVariousIteratorArgs::test_inline_methods, test/inductor/test_ordered_set.py::TestVariousIteratorArgs::test_inplace_methods, test/inductor/test_ordered_set.py::TestWeirdBugs::test_8420_set_merge, test/inductor/test_ordered_set.py::TestWeirdBugs::test_iter_and_mutate, test/inductor/test_ordered_set.py::TestWeirdBugs::test_merge_and_mutate, test/inductor/test_ordered_set.py::TestGraphs::test_cube, test/inductor/test_ordered_set.py::TestGraphs::test_cuboctahedron
2025-03-14T05:32:18.6542133Z 
2025-03-14T05:32:19.6520717Z Running nn/test_packed_sequence 1/1 ... [2025-03-14 05:32:19.651724]
2025-03-14T05:32:19.6521301Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:32:19.6522870Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_packed_sequence.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:32:19.652063]
2025-03-14T05:32:22.2631750Z Running dynamo/test_metrics_context 1/1 ... [2025-03-14 05:32:22.262822]
2025-03-14T05:32:22.2632288Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:32:22.2634997Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_metrics_context.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:32:22.263248]
2025-03-14T05:32:23.4718101Z 
2025-03-14T05:32:23.4719311Z nn/test_packed_sequence 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_packed_sequence_1.1_f74a602a50319c32_.log
2025-03-14T05:32:23.4724026Z Running 12 items in this shard: test/nn/test_packed_sequence.py::PackedSequenceTest::test_pack_padded_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pack_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pad_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pad_sequence_with_non_iterable_sequences, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pad_sequence_with_tensor_sequences, test/nn/test_packed_sequence.py::PackedSequenceTest::test_to, test/nn/test_packed_sequence.py::PackedSequenceTest::test_to_memory_format, test/nn/test_packed_sequence.py::PackedSequenceTest::test_total_length, test/nn/test_packed_sequence.py::PackedSequenceTest::test_type_casts, test/nn/test_packed_sequence.py::PackedSequenceTest::test_unpack_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_unpad_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_wrong_order
2025-03-14T05:32:23.4728022Z 
2025-03-14T05:32:26.0331954Z 
2025-03-14T05:32:26.0333094Z dynamo/test_metrics_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_metrics_context_1.1_ce65eea2d76217e4_.log
2025-03-14T05:32:26.0337165Z Running 9 items in this shard: test/dynamo/test_metrics_context.py::TestMetricsContext::test_add_to_set, test/dynamo/test_metrics_context.py::TestMetricsContext::test_context_exists, test/dynamo/test_metrics_context.py::TestMetricsContext::test_nested_context, test/dynamo/test_metrics_context.py::TestMetricsContext::test_set, test/dynamo/test_metrics_context.py::TestMetricsContext::test_set_disallow_overwrite, test/dynamo/test_metrics_context.py::TestMetricsContext::test_set_key_value, test/dynamo/test_metrics_context.py::TestMetricsContext::test_top_n, test/dynamo/test_metrics_context.py::TestMetricsContext::test_update_allow_overwrite, test/dynamo/test_metrics_context.py::TestMetricsContext::test_update_disallow_overwrite
2025-03-14T05:32:26.0340434Z 
2025-03-14T05:32:27.0460977Z Running dynamo/test_base_hop 1/1 ... [2025-03-14 05:32:27.045755]
2025-03-14T05:32:27.0461522Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:32:27.0463287Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_hop.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:32:27.046101]
2025-03-14T05:32:29.6128600Z Running test_custom_ops 1/1 ... [2025-03-14 05:32:29.612477]
2025-03-14T05:32:29.6129105Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:32:29.6131430Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_custom_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:32:29.612840]
2025-03-14T05:32:34.8713941Z 
2025-03-14T05:32:34.8714886Z dynamo/test_base_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_hop_1.1_f8d0cb0a85f143f2_.log
2025-03-14T05:32:34.8716657Z Running 4 items in this shard: test/dynamo/test_base_hop.py::BaseHOPTest::test_aliasing_mutation_error, test/dynamo/test_base_hop.py::BaseHOPTest::test_aot_eager, test/dynamo/test_base_hop.py::BaseHOPTest::test_dynamo, test/dynamo/test_base_hop.py::BaseHOPTest::test_eager_call
2025-03-14T05:32:34.8717811Z 
2025-03-14T05:32:38.4381224Z Running dynamo/test_modules 1/1 ... [2025-03-14 05:32:38.437696]
2025-03-14T05:32:38.4381751Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:32:38.4382893Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:32:38.438056]
2025-03-14T05:32:57.8205199Z 
2025-03-14T05:32:57.8206162Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_d829221cecde88d8_.log
2025-03-14T05:32:57.8288797Z Running 264 items in this shard: test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_CompositeExplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_meta, test/test_custom_ops.py::TestCustomOp::test_autogen_aten_ops_are_pt2_compliant, test/test_custom_ops.py::TestCustomOp::test_autograd_function_backed_op, test/test_custom_ops.py::TestCustomOp::test_autograd_notimplemented, test/test_custom_ops.py::TestCustomOp::test_autograd_notimplemented_gradmode, test/test_custom_ops.py::TestCustomOp::test_backward_dict_grad_for_nontensor, test/test_custom_ops.py::TestCustomOp::test_backward_dict_invalid_keys, test/test_custom_ops.py::TestCustomOp::test_backward_dict_requires_keys_for_input_optional_tensors, test/test_custom_ops.py::TestCustomOp::test_backward_dict_requires_keys_for_input_tensors, test/test_custom_ops.py::TestCustomOp::test_backward_grads_are_tensor_or_none, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_mutable, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_no_output, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_views, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_Autograd, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_AutogradCPU, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_AutogradCUDA, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_non_tensor, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_numel, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_tensorlist, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_type, test/test_custom_ops.py::TestCustomOp::test_backward_partially_registered, test/test_custom_ops.py::TestCustomOp::test_backward_returns_dict, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads_none_or_Tensor, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads_with_same_numel, test/test_custom_ops.py::TestCustomOp::test_basic_make_fx, test/test_custom_ops.py::TestCustomOp::test_builtin_aten_ops_are_pt2_compliant, test/test_custom_ops.py::TestCustomOp::test_builtin_torchscript_ops, test/test_custom_ops.py::TestCustomOp::test_data_dependent_basic, test/test_custom_ops.py::TestCustomOp::test_data_dependent_compile, test/test_custom_ops.py::TestCustomOp::test_data_dependent_fake_tracing, test/test_custom_ops.py::TestCustomOp::test_data_dependent_nms_dynamic_compile, test/test_custom_ops.py::TestCustomOp::test_define_and_impl, test/test_custom_ops.py::TestCustomOp::test_define_bad_schema, test/test_custom_ops.py::TestCustomOp::test_define_validation, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_list, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_single, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_tuple, test/test_custom_ops.py::TestCustomOp::test_defined_in_python, test/test_custom_ops.py::TestCustomOp::test_deploy_interaction, test/test_custom_ops.py::TestCustomOp::test_duplicate_impl, test/test_custom_ops.py::TestCustomOp::test_functionalize_error, test/test_custom_ops.py::TestCustomOp::test_impl_abstract_overload, test/test_custom_ops.py::TestCustomOp::test_impl_cpu, test/test_custom_ops.py::TestCustomOp::test_impl_device_cpu, test/test_custom_ops.py::TestCustomOp::test_impl_device_cuda, test/test_custom_ops.py::TestCustomOp::test_impl_device_function, test/test_custom_ops.py::TestCustomOp::test_impl_device_invalid, test/test_custom_ops.py::TestCustomOp::test_impl_function, test/test_custom_ops.py::TestCustomOp::test_impl_invalid_devices, test/test_custom_ops.py::TestCustomOp::test_impl_meta, test/test_custom_ops.py::TestCustomOp::test_impl_multiple, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CPU, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CUDA, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CompositeExplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_impl_separate, test/test_custom_ops.py::TestCustomOp::test_incorrect_schema_types, test/test_custom_ops.py::TestCustomOp::test_infer_schema_no_return, test/test_custom_ops.py::TestCustomOp::test_infer_schema_supported, test/test_custom_ops.py::TestCustomOp::test_infer_schema_unsupported, test/test_custom_ops.py::TestCustomOp::test_invalid_qualname, test/test_custom_ops.py::TestCustomOp::test_invalid_schemas, test/test_custom_ops.py::TestCustomOp::test_is_functional_schema, test/test_custom_ops.py::TestCustomOp::test_is_tensorlist_like_type, test/test_custom_ops.py::TestCustomOp::test_legacy_define, test/test_custom_ops.py::TestCustomOp::test_legacy_impl, test/test_custom_ops.py::TestCustomOp::test_lifetime, test/test_custom_ops.py::TestCustomOp::test_meta_for_data_dependent_shape_operation, test/test_custom_ops.py::TestCustomOp::test_name_must_match, test/test_custom_ops.py::TestCustomOp::test_new_data_dependent_symint, test/test_custom_ops.py::TestCustomOp::test_not_implemented_error, test/test_custom_ops.py::TestCustomOp::test_private_ctor, test/test_custom_ops.py::TestCustomOp::test_reserved_ns, test/test_custom_ops.py::TestCustomOp::test_resolve_packet, test/test_custom_ops.py::TestCustomOp::test_save_for_backward_inputs_are_namedtuple, test/test_custom_ops.py::TestCustomOp::test_schema_matches_signature, test/test_custom_ops.py::TestCustomOp::test_sequences, test/test_custom_ops.py::TestCustomOp::test_supported_param_types, test/test_custom_ops.py::TestCustomOp::test_supported_return_types_multi_return, test/test_custom_ops.py::TestCustomOp::test_supported_return_types_single_return, test/test_custom_ops.py::TestCustomOp::test_supported_schemas, test/test_custom_ops.py::TestCustomOp::test_symints, test/test_custom_ops.py::TestCustomOp::test_unsupported_param_types, test/test_custom_ops.py::TestCustomOp::test_unsupported_schemas, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_inplace, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_inplace, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_inplace, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_dont_generate, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_inplace, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_inplace, test/test_custom_ops.py::MiniOpTest::test_mm, test/test_custom_ops.py::MiniOpTest::test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_nonzero, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_mm, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_nonzero, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_sin_, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_schema__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_schema__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_schema__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_schema__test_inplace, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_schema__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_schema__test_nonzero, test/test_custom_ops.py::TestCustomOpAPI::test_basic, test/test_custom_ops.py::TestCustomOpAPI::test_compile, test/test_custom_ops.py::TestCustomOpAPI::test_default_values, test/test_custom_ops.py::TestCustomOpAPI::test_disallows_output_aliasing, test/test_custom_ops.py::TestCustomOpAPI::test_factory_function, test/test_custom_ops.py::TestCustomOpAPI::test_fake, test/test_custom_ops.py::TestCustomOpAPI::test_kwarg_only_tensors, test/test_custom_ops.py::TestCustomOpAPI::test_layout_constraint_tags, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_list_input, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_multiple_times, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_multiple_times_different_devices, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autograd, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autograd_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_0, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_1, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_2, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_3, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_4, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_5, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_kernel, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_kernel_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_rule_mode, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_rule_subclass, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_library_decorator, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_op_decorator, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_register_multiple_times, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_register_multiple_times_2, test/test_custom_ops.py::TestCustomOpAPI::test_library_schema_infer, test/test_custom_ops.py::TestCustomOpAPI::test_manual_schema, test/test_custom_ops.py::TestCustomOpAPI::test_manual_schema_error, test/test_custom_ops.py::TestCustomOpAPI::test_multi_types, test/test_custom_ops.py::TestCustomOpAPI::test_mutated, test/test_custom_ops.py::TestCustomOpAPI::test_mutated_error, test/test_custom_ops.py::TestCustomOpAPI::test_mutated_unknown, test/test_custom_ops.py::TestCustomOpAPI::test_no_grad_skips_autograd, test/test_custom_ops.py::TestCustomOpAPI::test_overloading, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_defaults, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_error_cases, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_kwargonly_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_register_vmap_defaults, test/test_custom_ops.py::TestCustomOpAPI::test_register_vmap_kwargonly_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_replacement, test/test_custom_ops.py::TestCustomOpAPI::test_set_kernel_enabled, test/test_custom_ops.py::TestCustomOpAPI::test_split_device, test/test_custom_ops.py::TestCustomOpAPI::test_supports_tensorlist, test/test_custom_ops.py::MiniOpTestOther::test_aot_dispatch_dynamic__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_aot_dispatch_static__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_autograd_registration__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_faketensor__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_mm, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_nonzero, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_sin_, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_delayed_error, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_incorrect_schema, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_no_abstract, test/test_custom_ops.py::MiniOpTestOther::test_schema__test_nonzero_again, test/test_custom_ops.py::TestGenerateOpcheckTests::test_MiniOpTest, test/test_custom_ops.py::TestGenerateOpcheckTests::test_dont_generate_decorator, test/test_custom_ops.py::TestGenerateOpcheckTests::test_failures_dict_validation, test/test_custom_ops.py::TestGenerateOpcheckTests::test_generate_repro_no_save_data, test/test_custom_ops.py::TestGenerateOpcheckTests::test_generate_repro_save_data, test/test_custom_ops.py::TestGenerateOpcheckTests::test_is_inside_opcheck_mode, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_bad_op, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_customopdef, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_does_not_require_extra_deps, test/test_custom_ops.py::TestTypeConversion::test_mixed_types, test/test_custom_ops.py::TestTypeConversion::test_optional, test/test_custom_ops.py::TestTypeConversion::test_simple_tuple, test/test_custom_ops.py::TestTypeConversion::test_supported_types, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_False_dynamic_False_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_False_dynamic_True_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_auto_dynamic_False_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_auto_dynamic_True_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_assert_raises_regex_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registered_at_backend_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_autograd_kernel_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_compositeimplicitautograd_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_incorrect_composite_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_incorrect_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_global_state_mutation_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_abstract_impl_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_schema_mutation_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_schema_view_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_missing_abstract_impl_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_missing_functionalization_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_fails_basic_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyCatCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyCubeCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyMulCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyMulScalarCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyNMSCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyNonzeroCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySortCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySplitCopyCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyTakeCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyViewCopyCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_unbacked_stride_cpu
2025-03-14T05:32:57.8370060Z 
2025-03-14T05:33:01.4015772Z Running inductor/test_fused_attention 1/1 ... [2025-03-14 05:33:01.401191]
2025-03-14T05:33:01.4016608Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:33:01.4017780Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fused_attention.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:33:01.401517]
2025-03-14T05:33:05.8413975Z 
2025-03-14T05:33:05.8415301Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_b4a489adadf4b1ad_.log
2025-03-14T05:33:05.8453769Z Running 127 items in this shard: test/dynamo/test_modules.py::NNModuleTests::test_access_by_keys, test/dynamo/test_modules.py::NNModuleTests::test_basicmodule1, test/dynamo/test_modules.py::NNModuleTests::test_basicmodule2, test/dynamo/test_modules.py::NNModuleTests::test_call_fn_with_non_const_inputs_safe, test/dynamo/test_modules.py::NNModuleTests::test_cfgmod, test/dynamo/test_modules.py::NNModuleTests::test_children, test/dynamo/test_modules.py::NNModuleTests::test_constloop, test/dynamo/test_modules.py::NNModuleTests::test_conv_call_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_conv_call_super_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_conv_transpose_call_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_conv_transpose_call_super_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_densenet, test/dynamo/test_modules.py::NNModuleTests::test_enumvalues, test/dynamo/test_modules.py::NNModuleTests::test_fnmember, test/dynamo/test_modules.py::NNModuleTests::test_fnmembercmp1, test/dynamo/test_modules.py::NNModuleTests::test_fnmembercmp2, test/dynamo/test_modules.py::NNModuleTests::test_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_generation_tag, test/dynamo/test_modules.py::NNModuleTests::test_hasattr, test/dynamo/test_modules.py::NNModuleTests::test_inject_module_parameters, test/dynamo/test_modules.py::NNModuleTests::test_intarg, test/dynamo/test_modules.py::NNModuleTests::test_iseval1, test/dynamo/test_modules.py::NNModuleTests::test_iseval2, test/dynamo/test_modules.py::NNModuleTests::test_isnonelayer, test/dynamo/test_modules.py::NNModuleTests::test_istraining1, test/dynamo/test_modules.py::NNModuleTests::test_istraining2, test/dynamo/test_modules.py::NNModuleTests::test_layerlist, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module1, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module2, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module4, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module5, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module6, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module7, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_kwargs, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_no_cls_to_become, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_speculation_log_divergence, test/dynamo/test_modules.py::NNModuleTests::test_module_attribute_precedence, test/dynamo/test_modules.py::NNModuleTests::test_module_call_module_with_static_forward, test/dynamo/test_modules.py::NNModuleTests::test_module_class_method, test/dynamo/test_modules.py::NNModuleTests::test_module_comparison, test/dynamo/test_modules.py::NNModuleTests::test_module_forward_has_graph_break, test/dynamo/test_modules.py::NNModuleTests::test_module_guard_name_is_valid, test/dynamo/test_modules.py::NNModuleTests::test_module_name_string, test/dynamo/test_modules.py::NNModuleTests::test_module_property, test/dynamo/test_modules.py::NNModuleTests::test_module_static_method, test/dynamo/test_modules.py::NNModuleTests::test_moduledict, test/dynamo/test_modules.py::NNModuleTests::test_moduledict_custom, test/dynamo/test_modules.py::NNModuleTests::test_modulelist, test/dynamo/test_modules.py::NNModuleTests::test_modulelist_custom, test/dynamo/test_modules.py::NNModuleTests::test_modulelist_nested, test/dynamo/test_modules.py::NNModuleTests::test_modulemethod1, test/dynamo/test_modules.py::NNModuleTests::test_modulemethod2, test/dynamo/test_modules.py::NNModuleTests::test_named_children, test/dynamo/test_modules.py::NNModuleTests::test_nn_module_setattr, test/dynamo/test_modules.py::NNModuleTests::test_nn_module_unspec_int_attr, test/dynamo/test_modules.py::NNModuleTests::test_nn_moduledict_contains, test/dynamo/test_modules.py::NNModuleTests::test_parameterdict, test/dynamo/test_modules.py::NNModuleTests::test_parameterdict_custom, test/dynamo/test_modules.py::NNModuleTests::test_parameters1, test/dynamo/test_modules.py::NNModuleTests::test_parameters2, test/dynamo/test_modules.py::NNModuleTests::test_parameters3, test/dynamo/test_modules.py::NNModuleTests::test_parameters4, test/dynamo/test_modules.py::NNModuleTests::test_parameters5, test/dynamo/test_modules.py::NNModuleTests::test_self_mutating1, test/dynamo/test_modules.py::NNModuleTests::test_seq, test/dynamo/test_modules.py::NNModuleTests::test_sequential_with_duplicated_module, test/dynamo/test_modules.py::NNModuleTests::test_sequential_with_duplicated_module2, test/dynamo/test_modules.py::NNModuleTests::test_simple_torch_function, test/dynamo/test_modules.py::NNModuleTests::test_stringmember, test/dynamo/test_modules.py::NNModuleTests::test_submodules1, test/dynamo/test_modules.py::NNModuleTests::test_submodules2, test/dynamo/test_modules.py::NNModuleTests::test_super1, test/dynamo/test_modules.py::NNModuleTests::test_super2, test/dynamo/test_modules.py::NNModuleTests::test_super_class_method, test/dynamo/test_modules.py::NNModuleTests::test_tensorlist, test/dynamo/test_modules.py::NNModuleTests::test_torch_function_with_closure, test/dynamo/test_modules.py::NNModuleTests::test_torch_mangled_class_name, test/dynamo/test_modules.py::NNModuleTests::test_unsupportedmethod, test/dynamo/test_modules.py::NNModuleTests::test_unsupportedmodule, test/dynamo/test_modules.py::NNModuleTests::test_viamodulecall, test/dynamo/test_modules.py::OptimizedModuleTest::test_assign_does_not_exist, test/dynamo/test_modules.py::OptimizedModuleTest::test_attr, test/dynamo/test_modules.py::OptimizedModuleTest::test_attr_precedence, test/dynamo/test_modules.py::OptimizedModuleTest::test_backward_hooks, test/dynamo/test_modules.py::OptimizedModuleTest::test_branch_on_nn_module_custom_bool, test/dynamo/test_modules.py::OptimizedModuleTest::test_branch_on_nn_module_custom_len, test/dynamo/test_modules.py::OptimizedModuleTest::test_buffer_order, test/dynamo/test_modules.py::OptimizedModuleTest::test_composition, test/dynamo/test_modules.py::OptimizedModuleTest::test_composition_with_opt_mod, test/dynamo/test_modules.py::OptimizedModuleTest::test_dir, test/dynamo/test_modules.py::OptimizedModuleTest::test_dunder_call_explicitly, test/dynamo/test_modules.py::OptimizedModuleTest::test_globals_change_in_other_file, test/dynamo/test_modules.py::OptimizedModuleTest::test_guard_on_torch_nn_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_allowed_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_allowed_modules_compiles, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_allowed_modules_compiles_self_contained, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_inner, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_outer, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_skip_guards, test/dynamo/test_modules.py::OptimizedModuleTest::test_inline_inbuilt_nn_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_mark_static_nn_module_tensor, test/dynamo/test_modules.py::OptimizedModuleTest::test_mark_static_previously_seen_tensor, test/dynamo/test_modules.py::OptimizedModuleTest::test_mark_static_with_freezing, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_dict_iter_keys, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_dict_iter_name, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_dict_iter_values, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_order, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_patch, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_setattr, test/dynamo/test_modules.py::OptimizedModuleTest::test_monkeypatching_forward, test/dynamo/test_modules.py::OptimizedModuleTest::test_nn_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_no_op_assignment, test/dynamo/test_modules.py::OptimizedModuleTest::test_no_recompile_on_nn_guarded_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_overridden_call, test/dynamo/test_modules.py::OptimizedModuleTest::test_param_order, test/dynamo/test_modules.py::OptimizedModuleTest::test_param_requires_grad, test/dynamo/test_modules.py::OptimizedModuleTest::test_recompile_limit_on_freed_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_recompile_limit_on_guarded_nn_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_recursion, test/dynamo/test_modules.py::OptimizedModuleTest::test_save_and_load_all_backends, test/dynamo/test_modules.py::OptimizedModuleTest::test_save_and_load_inductor, test/dynamo/test_modules.py::OptimizedModuleTest::test_setattr_on_compiled_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_to, test/dynamo/test_modules.py::OptimizedModuleTest::test_udo_instance_method_as_hook, test/dynamo/test_modules.py::OptimizedModuleTest::test_unspec_non_inlinable_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_unspecialized_seq, test/dynamo/test_modules.py::OptimizedModuleTest::test_user_defined_nn_module_dynamic
2025-03-14T05:33:05.8491344Z 
2025-03-14T05:33:09.4137980Z Running inductor/test_layout_optim 1/1 ... [2025-03-14 05:33:09.413386]
2025-03-14T05:33:09.4138584Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:33:09.4140299Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_layout_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:33:09.413756]
2025-03-14T05:33:16.5455929Z 
2025-03-14T05:33:16.5456988Z inductor/test_layout_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_layout_optim_1.1_a7a7f049ef28a340_.log
2025-03-14T05:33:16.5457728Z 
2025-03-14T05:33:20.2505987Z Running inductor/test_inductor_freezing 1/1 ... [2025-03-14 05:33:20.250166]
2025-03-14T05:33:20.2506926Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:33:20.2510185Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_freezing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:33:20.250511]
2025-03-14T05:34:20.9136184Z 
2025-03-14T05:34:20.9137830Z inductor/test_inductor_freezing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_freezing_1.1_b9c998739e9cfd9d_.log
2025-03-14T05:34:20.9156688Z Running 23 items in this shard: test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_aliased_param_return_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_autocast_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_functional_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_layout_convert_with_view_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_multiple_uses_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_weight_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_with_as_strided_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_cpp_wrapper_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_dont_change_dtype_folding_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_error_on_eager_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_hardswish_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_functional_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mm_concat_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mutation_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_param_deallocated_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_redundant_clone_for_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_rng_op_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_symint_not_folded_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unequal_bias_horizontal_addmm_fusion_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unfolded_bn_cpu
2025-03-14T05:34:20.9173845Z 
2025-03-14T05:34:24.8074241Z Running inductor/test_mmdecomp 1/1 ... [2025-03-14 05:34:24.807061]
2025-03-14T05:34:24.8075124Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:34:24.8076439Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_mmdecomp.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:34:24.807398]
2025-03-14T05:34:32.0829805Z 
2025-03-14T05:34:32.0830975Z inductor/test_mmdecomp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mmdecomp_1.1_8c99c095a110a0bb_.log
2025-03-14T05:34:32.0842424Z Running 22 items in this shard: test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_10_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_1_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_2_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_4_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_bmm_batch2_last_dim_size_is_one_cpu, test/inductor/test_mmdecomp.py::TestDecompCPU::test_simple_mm_bfloat16_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_simple_mm_float32_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_10_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_1_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_2_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_4_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_10_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_1_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_2_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_4_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_10_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_1_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_2_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_4_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_bfloat16_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_float32_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_int32_cpu_int32
2025-03-14T05:34:32.0850661Z 
2025-03-14T05:34:35.7928118Z Running inductor/test_binary_folding 1/1 ... [2025-03-14 05:34:35.792397]
2025-03-14T05:34:35.7929043Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:34:35.7931858Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_binary_folding.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:34:35.792740]
2025-03-14T05:35:41.2577875Z 
2025-03-14T05:35:41.2578862Z inductor/test_fused_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fused_attention_1.1_027ee86a99aac6eb_.log
2025-03-14T05:35:41.2596335Z Running 38 items in this shard: test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_pattern_fails_with_reuse_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_pattern_fails_with_tensor_factor_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_pattern_fails_with_unsupported_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_prev_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_prev_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_prev_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_11_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_12_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_16_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_16_fp32_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_17_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_18_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_19_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_1_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_2_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_5_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_pattern_fails_with_reuse_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_pattern_fails_with_tensor_factor_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_pattern_fails_with_unsupported_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_prev_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_prev_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_prev_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_11_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_12_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_16_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_16_fp32_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_17_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_18_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_19_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_1_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_2_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_5_cpu
2025-03-14T05:35:41.2613264Z 
2025-03-14T05:35:44.8741830Z Running dynamo/test_dicts 1/1 ... [2025-03-14 05:35:44.873793]
2025-03-14T05:35:44.8742669Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:35:44.8744768Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_dicts.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:35:44.874141]
2025-03-14T05:35:50.0972929Z 
2025-03-14T05:35:50.0974375Z dynamo/test_dicts 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dicts_1.1_44a5d0883a604587_.log
2025-03-14T05:35:50.0999443Z Running 44 items in this shard: test/dynamo/test_dicts.py::DictTests::test_contains_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_contains_module_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_custom_iter_dict, test/dynamo/test_dicts.py::DictTests::test_custom_keys_iter_dict, test/dynamo/test_dicts.py::DictTests::test_dict_contains, test/dynamo/test_dicts.py::DictTests::test_dict_copy_alias, test/dynamo/test_dicts.py::DictTests::test_dict_guard_on_keys_order, test/dynamo/test_dicts.py::DictTests::test_dict_guard_on_keys_order2, test/dynamo/test_dicts.py::DictTests::test_dict_iter, test/dynamo/test_dicts.py::DictTests::test_dict_list_values, test/dynamo/test_dicts.py::DictTests::test_dict_mutation_side_effect, test/dynamo/test_dicts.py::DictTests::test_dict_namedtuple, test/dynamo/test_dicts.py::DictTests::test_dict_order_keys, test/dynamo/test_dicts.py::DictTests::test_dict_order_keys_modules, test/dynamo/test_dicts.py::DictTests::test_dict_order_keys_tensors, test/dynamo/test_dicts.py::DictTests::test_dict_reconstruct_keeps_original_order, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_contains, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_get_method, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_initialization_in_graph, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_instantiation, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_instantiation_return, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_local_mutation, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_local_with_non_dict_method, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_methods_fallback_mutation, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_methods_fallback_readonly, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_setitem, test/dynamo/test_dicts.py::DictTests::test_dict_tag_guard, test/dynamo/test_dicts.py::DictTests::test_empty_dict_recompilation, test/dynamo/test_dicts.py::DictTests::test_fn_id, test/dynamo/test_dicts.py::DictTests::test_lazy_key_guarding, test/dynamo/test_dicts.py::DictTests::test_lazy_key_non_const_guarding, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_existing, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_existing_local_mutation, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_existing_mutation, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_for_local, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_for_nonlocal, test/dynamo/test_dicts.py::DictTests::test_move_to_end, test/dynamo/test_dicts.py::DictTests::test_ordered_dict_reordered_keys, test/dynamo/test_dicts.py::DictTests::test_ordered_dict_subclass_reordered_keys, test/dynamo/test_dicts.py::DictTests::test_overridden_get_item, test/dynamo/test_dicts.py::DictTests::test_udf_dict_reconstruction, test/dynamo/test_dicts.py::DictTests::test_update_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_update_module_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_weakref_dict
2025-03-14T05:35:50.1023080Z 
2025-03-14T05:35:53.7530308Z Running dynamo/test_compiler_bisector 1/1 ... [2025-03-14 05:35:53.752588]
2025-03-14T05:35:53.7531238Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:35:53.7533873Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:35:53.753045]
2025-03-14T05:36:01.0275718Z 
2025-03-14T05:36:01.0277426Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_c947626802db735c_.log
2025-03-14T05:36:01.0283516Z Running 7 items in this shard: test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_bad_decomp, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_bad_lowering, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_crossref, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_eager_backend, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_emulate_precision_casts, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_joint_graph, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng
2025-03-14T05:36:01.0288566Z 
2025-03-14T05:36:04.7222108Z Running test_legacy_vmap 1/1 ... [2025-03-14 05:36:04.721807]
2025-03-14T05:36:04.7222900Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:04.7226628Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:04.722304]
2025-03-14T05:36:08.4010003Z 
2025-03-14T05:36:08.4011680Z inductor/test_binary_folding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_binary_folding_1.1_203c4b95df6d01d8_.log
2025-03-14T05:36:08.4015155Z Running 3 items in this shard: test/inductor/test_binary_folding.py::FreezingCpuTests::test_conv_binary_folding_cpu, test/inductor/test_binary_folding.py::FreezingCpuTests::test_conv_bn_folding_cpu, test/inductor/test_binary_folding.py::FreezingCpuTests::test_linear_binary_folding_cpu
2025-03-14T05:36:08.4017478Z 
2025-03-14T05:36:10.0942232Z 
2025-03-14T05:36:10.0943826Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_7dd8973b6b65d89a_.log
2025-03-14T05:36:10.1017780Z Running 124 items in this shard: test/test_legacy_vmap.py::TestVmapAPILegacy::test_accepts_nested_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_backward_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_batched_gradient_basic, test/test_legacy_vmap.py::TestVmapAPILegacy::test_constant_function, test/test_legacy_vmap.py::TestVmapAPILegacy::test_different_map_dim_size_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_atan2, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_does_not_warn_by_default, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_masked_fill, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_multiple_returns, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_warns_when_warnings_are_enabled, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_with_undefined_grad, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_zero_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_func_with_no_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_functools_partial, test/test_legacy_vmap.py::TestVmapAPILegacy::test_grad_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dim_not_in_tensor_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dims_wrong_type_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_different_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_same_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_unary, test/test_legacy_vmap.py::TestVmapAPILegacy::test_integer_in_dim_but_not_tensor_input_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs_error_cases, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_non_default_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_different_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_same_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nn_module, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_default_in_dims_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_tensor_output_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_zero_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_none_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nonzero_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_noop_in_inner_vmap, test/test_legacy_vmap.py::TestVmapAPILegacy::test_not_enough_in_dims_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dim_out_of_bounds_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_and_num_outputs_mismatch_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_edge_case, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_must_be_int_or_tuple_of_int_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_single_input, test/test_legacy_vmap.py::TestVmapAPILegacy::test_unsupported_op_err_msg, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_T_numpy, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_as_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_binary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_bmm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_cat, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_chunk, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clamp, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clone, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_comparison_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_conj, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_diagonal, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_dot, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_expand_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_fill_and_zero_inplace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_imag, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_floating_point, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_movedim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mv, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_narrow, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_zeros, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_no_random_op_support, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_result_type, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_select, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_slice, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_squeeze, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stack, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stride, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_sum_dim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_t, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_tensor_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_to, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_trace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_transpose, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unbind, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unfold, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check_ok, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_add_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_binary_cross_entropy_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_diagonal_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_div_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_expand_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_index_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_manyview_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_on_view_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_lgamma_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log1p_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_logsumexp_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_max_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_median_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_min_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_mul_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_permute_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_reshape_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_select_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sigmoid_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_slice_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_stack_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sub_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_threshold_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_trace_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_multiple_grad_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check_ok
2025-03-14T05:36:10.1090237Z 
2025-03-14T05:36:12.1513031Z Running test_cuda_sanitizer 1/1 ... [2025-03-14 05:36:12.150921]
2025-03-14T05:36:12.1513563Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:12.1515324Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_sanitizer.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:12.151253]
2025-03-14T05:36:13.6918515Z Running test_pytree 1/1 ... [2025-03-14 05:36:13.691453]
2025-03-14T05:36:13.6919083Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:13.6920779Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_pytree.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:13.691803]
2025-03-14T05:36:15.7859581Z 
2025-03-14T05:36:15.7860774Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_294b4b2bdc7fa80c_.log
2025-03-14T05:36:15.7861578Z Running 0 items in this shard:
2025-03-14T05:36:15.7861794Z 
2025-03-14T05:36:19.2135479Z 
2025-03-14T05:36:19.2136532Z test_pytree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_pytree_1.1_3bacf52bd586d4bb_.log
2025-03-14T05:36:19.2163737Z Running 91 items in this shard: test/test_pytree.py::TestGenericPytree::test_aligned_public_apis, test/test_pytree.py::TestGenericPytree::test_broadcast_to_and_flatten_cxx, test/test_pytree.py::TestGenericPytree::test_broadcast_to_and_flatten_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_defaultdict_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_defaultdict_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_deque_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_deque_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_dict_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_dict_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_leaf_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_leaf_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_list_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_list_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_namedtuple_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_namedtuple_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_nested_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_nested_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_ordereddict_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_ordereddict_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_max_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_max_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_min_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_min_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_tuple_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_tuple_py, test/test_pytree.py::TestGenericPytree::test_flatten_with_is_leaf_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_with_is_leaf_py, test/test_pytree.py::TestGenericPytree::test_pytree_serialize_bad_input_cxx, test/test_pytree.py::TestGenericPytree::test_pytree_serialize_bad_input_py, test/test_pytree.py::TestGenericPytree::test_register_pytree_node_cxx, test/test_pytree.py::TestGenericPytree::test_register_pytree_node_py, test/test_pytree.py::TestGenericPytree::test_tree_all_any_cxx, test/test_pytree.py::TestGenericPytree::test_tree_all_any_py, test/test_pytree.py::TestGenericPytree::test_tree_map_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_multi_inputs_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_multi_inputs_py, test/test_pytree.py::TestGenericPytree::test_tree_map_only_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_only_predicate_fn_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_only_predicate_fn_py, test/test_pytree.py::TestGenericPytree::test_tree_map_only_py, test/test_pytree.py::TestGenericPytree::test_tree_map_py, test/test_pytree.py::TestPythonPytree::test_constant, test/test_pytree.py::TestPythonPytree::test_constant_default_eq_error, test/test_pytree.py::TestPythonPytree::test_constant_default_hash_error, test/test_pytree.py::TestPythonPytree::test_dataclass, test/test_pytree.py::TestPythonPytree::test_deprecated_register_pytree_node, test/test_pytree.py::TestPythonPytree::test_flatten_flatten_with_key_consistency, test/test_pytree.py::TestPythonPytree::test_import_pytree_doesnt_import_optree, test/test_pytree.py::TestPythonPytree::test_key_access, test/test_pytree.py::TestPythonPytree::test_key_str, test/test_pytree.py::TestPythonPytree::test_pytree_context_serialize_bad, test/test_pytree.py::TestPythonPytree::test_pytree_custom_type_serialize, test/test_pytree.py::TestPythonPytree::test_pytree_custom_type_serialize_bad, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_bad_protocol, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_defaultdict_enum, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_enum, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_namedtuple, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_namedtuple_bad, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_register_bad, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec0, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec1, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec2, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec3, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec4, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec5, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec6, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec7, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec8, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec9, test/test_pytree.py::TestPythonPytree::test_saved_serialized, test/test_pytree.py::TestPythonPytree::test_tree_flatten_with_path_is_leaf, test/test_pytree.py::TestPythonPytree::test_tree_flatten_with_path_roundtrip, test/test_pytree.py::TestPythonPytree::test_tree_leaves_with_path, test/test_pytree.py::TestPythonPytree::test_tree_map_with_path, test/test_pytree.py::TestPythonPytree::test_tree_map_with_path_multiple_trees, test/test_pytree.py::TestPythonPytree::test_treespec_equality, test/test_pytree.py::TestPythonPytree::test_treespec_repr, test/test_pytree.py::TestCxxPytree::test_pytree_custom_type_serialize, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_namedtuple, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec0, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec1, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec2, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec3, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec4, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec5, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec6, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec7, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec8, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec9, test/test_pytree.py::TestCxxPytree::test_treespec_equality, test/test_pytree.py::TestCxxPytree::test_treespec_repr
2025-03-14T05:36:19.2189698Z 
2025-03-14T05:36:19.4654684Z Running dynamo/test_flat_apply 1/1 ... [2025-03-14 05:36:19.465149]
2025-03-14T05:36:19.4655167Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:19.4657008Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:19.465481]
2025-03-14T05:36:22.8057098Z Running export/test_passes 1/1 ... [2025-03-14 05:36:22.805356]
2025-03-14T05:36:22.8057622Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:22.8059617Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_passes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:22.805710]
2025-03-14T05:36:23.4856287Z 
2025-03-14T05:36:23.4857271Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_9124f4921a8ad60f_.log
2025-03-14T05:36:23.4859371Z Running 4 items in this shard: test/dynamo/test_flat_apply.py::FlatApplyTests::test_non_tensor_output, test/dynamo/test_flat_apply.py::FlatApplyTests::test_nonstrict_trace_captured_tensor_post_aot_graph, test/dynamo/test_flat_apply.py::FlatApplyTests::test_nonstrict_trace_dynamo_graph, test/dynamo/test_flat_apply.py::FlatApplyTests::test_simple
2025-03-14T05:36:23.4860839Z 
2025-03-14T05:36:27.0906540Z Running dynamo/test_aot_autograd 1/1 ... [2025-03-14 05:36:27.089357]
2025-03-14T05:36:27.0907396Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:27.0909497Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:27.089775]
2025-03-14T05:36:41.1373506Z 
2025-03-14T05:36:41.1374719Z inductor/test_torchinductor_opinfo 40/42 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_40.42_f54768bdeab79d4f_.log
2025-03-14T05:36:41.1415694Z Running 83 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__softmax_backward_data_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acos_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmv_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addr_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmin_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bmm_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cat_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cfloat_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cos_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eye_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_power_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmin_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_half_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isclose_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isfinite_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isneginf_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_lu_solve_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_multi_dot_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_norm_subgradients_at_zero_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_solve_triangular_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vecdot_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vector_norm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_amin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_fill_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mode_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nan_to_num_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_grid_sample_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_grid_sample_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_replicate_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pairwise_distance_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_unshuffle_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softsign_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_threshold_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_upsample_nearest_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_1_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ravel_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reciprocal_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsub_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_add_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_hamming_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_w_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i0e_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i1e_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_spherical_bessel_j0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sqrt_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_multiple_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_stack_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_mean_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_mean_unbiased_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tile_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_true_divide_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_uint32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vsplit_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vsplit_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_like_cpu_float16
2025-03-14T05:36:41.1456242Z 
2025-03-14T05:36:44.7794779Z Running inductor/test_split_cat_fx_aten_passes 1/1 ... [2025-03-14 05:36:44.779115]
2025-03-14T05:36:44.7795382Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:44.7797201Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_split_cat_fx_aten_passes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:44.779455]
2025-03-14T05:36:44.7987605Z 
2025-03-14T05:36:44.7988649Z export/test_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_passes_1.1_278e86790997a179_.log
2025-03-14T05:36:44.7997208Z Running 25 items in this shard: test/export/test_passes.py::TestPasses::test_constant_folding_pass, test/export/test_passes.py::TestPasses::test_custom_obj_tuple_out, test/export/test_passes.py::TestPasses::test_fakify_script_objects, test/export/test_passes.py::TestPasses::test_fakify_script_objects_properly_handle_containers, test/export/test_passes.py::TestPasses::test_functionalization_with_view_copy, test/export/test_passes.py::TestPasses::test_inline_, test/export/test_passes.py::TestPasses::test_math_ops, test/export/test_passes.py::TestPasses::test_move_to_device_pass, test/export/test_passes.py::TestPasses::test_predispatch_autocast, test/export/test_passes.py::TestPasses::test_predispatch_autocast_and_set_grad, test/export/test_passes.py::TestPasses::test_predispatch_set_grad, test/export/test_passes.py::TestPasses::test_remove_auto_functionalized_pass, test/export/test_passes.py::TestPasses::test_remove_auto_functionalized_pass_tuple, test/export/test_passes.py::TestPasses::test_remove_effect_token_kwargs, test/export/test_passes.py::TestPasses::test_runtime_assert_inline_constraints_for_cond, test/export/test_passes.py::TestPasses::test_runtime_assert_inline_constraints_for_item, test/export/test_passes.py::TestPasses::test_runtime_assert_inline_constraints_for_nonzero, test/export/test_passes.py::TestPasses::test_runtime_assert_multiple_dims, test/export/test_passes.py::TestPasses::test_runtime_assert_one_dim, test/export/test_passes.py::TestPasses::test_runtime_assert_some_dims_not_specified, test/export/test_passes.py::TestPasses::test_runtime_assert_some_inps_not_used, test/export/test_passes.py::TestPasses::test_sequential_split, test/export/test_passes.py::TestPasses::test_sequential_split_graph, test/export/test_passes.py::TestPasses::test_view_to_view_copy, test/export/test_passes.py::TestPasses::test_views_op_having_view_copy
2025-03-14T05:36:44.8005069Z 
2025-03-14T05:36:48.5319437Z Running export/test_converter 1/1 ... [2025-03-14 05:36:48.531531]
2025-03-14T05:36:48.5320238Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:48.5322325Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_converter.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:48.531887]
2025-03-14T05:36:52.1539304Z 
2025-03-14T05:36:52.1540927Z inductor/test_split_cat_fx_aten_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_split_cat_fx_aten_passes_1.1_8cd1e9a91b184611_.log
2025-03-14T05:36:52.1542560Z Running 2 items in this shard: test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_select_cat_post_grad, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_split_cat_post_grad
2025-03-14T05:36:52.1543457Z 
2025-03-14T05:36:55.7566842Z Running inductor/test_efficient_conv_bn_eval 1/1 ... [2025-03-14 05:36:55.756301]
2025-03-14T05:36:55.7567404Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:36:55.7569750Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_efficient_conv_bn_eval.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:36:55.756645]
2025-03-14T05:37:03.4824270Z 
2025-03-14T05:37:03.4825360Z inductor/test_efficient_conv_bn_eval 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_efficient_conv_bn_eval_1.1_b6babd5d6ada897a_.log
2025-03-14T05:37:03.4826647Z Running 1 items in this shard: test/inductor/test_efficient_conv_bn_eval.py::EfficientConvBNEvalCpuTests::test_basic_cpu
2025-03-14T05:37:03.4827211Z 
2025-03-14T05:37:07.0570622Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2025-03-14 05:37:07.056705]
2025-03-14T05:37:07.0571191Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:37:07.0572592Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backward_higher_order_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:37:07.057026]
2025-03-14T05:37:23.1442348Z 
2025-03-14T05:37:23.1443681Z dynamo/test_backward_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backward_higher_order_ops_1.1_e02e0ae3064caa24_.log
2025-03-14T05:37:23.1448216Z Running 7 items in this shard: test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_eager, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd_graph_breaks, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd_side_effect, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_make_bw, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_make_fx_forward_contrived
2025-03-14T05:37:23.1451769Z 
2025-03-14T05:37:24.7451780Z 
2025-03-14T05:37:24.7453364Z export/test_converter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_converter_1.1_1b171aec24d01efb_.log
2025-03-14T05:37:24.7468848Z Running 48 items in this shard: test/export/test_converter.py::TestConverter::test_aten___getitem___dict, test/export/test_converter.py::TestConverter::test_aten___getitem___list, test/export/test_converter.py::TestConverter::test_aten___is__, test/export/test_converter.py::TestConverter::test_aten___isnot__, test/export/test_converter.py::TestConverter::test_aten___not__, test/export/test_converter.py::TestConverter::test_aten_add_t, test/export/test_converter.py::TestConverter::test_aten_append_t, test/export/test_converter.py::TestConverter::test_aten_dim, test/export/test_converter.py::TestConverter::test_aten_floordiv, test/export/test_converter.py::TestConverter::test_aten_len, test/export/test_converter.py::TestConverter::test_aten_tensor_dtype_int, test/export/test_converter.py::TestConverter::test_aten_tensor_dynamic, test/export/test_converter.py::TestConverter::test_aten_tensor_prim_dtype, test/export/test_converter.py::TestConverter::test_aten_to_dtype_with_mutating_storage, test/export/test_converter.py::TestConverter::test_context_manager, test/export/test_converter.py::TestConverter::test_convert_func_without_param, test/export/test_converter.py::TestConverter::test_convert_if_basic, test/export/test_converter.py::TestConverter::test_convert_if_duplicate_attr_names, test/export/test_converter.py::TestConverter::test_convert_if_multiple_out, test/export/test_converter.py::TestConverter::test_convert_if_tuple_out, test/export/test_converter.py::TestConverter::test_convert_nn_module_with_nested_buffer, test/export/test_converter.py::TestConverter::test_convert_nn_module_with_nested_if_and_buffer, test/export/test_converter.py::TestConverter::test_convert_nn_module_with_nested_if_and_param, test/export/test_converter.py::TestConverter::test_convert_nn_module_with_nested_param, test/export/test_converter.py::TestConverter::test_convert_retrace_nested_scripted_modules, test/export/test_converter.py::TestConverter::test_convert_script_object, test/export/test_converter.py::TestConverter::test_get_tensor_constants, test/export/test_converter.py::TestConverter::test_hidden_input_name, test/export/test_converter.py::TestConverter::test_implicit_constant_to_tensor_handling, test/export/test_converter.py::TestConverter::test_prim_SetAttr, test/export/test_converter.py::TestConverter::test_prim_device, test/export/test_converter.py::TestConverter::test_prim_device_cuda, test/export/test_converter.py::TestConverter::test_prim_dtype, test/export/test_converter.py::TestConverter::test_prim_max, test/export/test_converter.py::TestConverter::test_prim_min, test/export/test_converter.py::TestConverter::test_prim_tolist, test/export/test_converter.py::TestConverter::test_profiler__record_function, test/export/test_converter.py::TestConverter::test_raise_exception, test/export/test_converter.py::TestConverter::test_ts2ep_convert_quantized_model, test/export/test_converter.py::TestConverter::test_ts2ep_convert_quantized_model_with_opcontext, test/export/test_converter.py::TestConverter::test_ts2ep_convert_quantized_model_with_opcontext_and_constant, test/export/test_converter.py::TestConverter::test_ts2ep_converter_basic, test/export/test_converter.py::TestConverter::test_ts2ep_converter_container_output, test/export/test_converter.py::TestConverter::test_ts2ep_converter_contains, test/export/test_converter.py::TestConverter::test_ts2ep_converter_custom_op, test/export/test_converter.py::TestConverter::test_ts2ep_converter_unpack, test/export/test_converter.py::TestConverter::test_ts2ep_multi_outputs_on_call_ops, test/export/test_converter.py::TestConverter::test_ts2ep_with_loop
2025-03-14T05:37:24.7483979Z 
2025-03-14T05:37:26.7084773Z Running test_autograd 1/1 ... [2025-03-14 05:37:26.708118]
2025-03-14T05:37:26.7085251Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:37:26.7087167Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:37:26.708451]
2025-03-14T05:37:28.3022972Z Running test_functional_optim 1/1 ... [2025-03-14 05:37:28.301926]
2025-03-14T05:37:28.3023708Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:37:28.3025370Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_functional_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:37:28.302303]
2025-03-14T05:37:32.1719084Z 
2025-03-14T05:37:32.1720088Z test_functional_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_optim_1.1_a5fe1f0b2b56fb16_.log
2025-03-14T05:37:32.1722458Z Running 4 items in this shard: test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_parity_adam, test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_parity_adam_w, test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_parity_sgd, test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_registration
2025-03-14T05:37:32.1725079Z 
2025-03-14T05:37:35.7044252Z Running dynamo/test_resume 1/1 ... [2025-03-14 05:37:35.704047]
2025-03-14T05:37:35.7045001Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:37:35.7047303Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_resume.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:37:35.704412]
2025-03-14T05:37:39.4238666Z 
2025-03-14T05:37:39.4239920Z dynamo/test_resume 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_resume_1.1_ed8ba463098a5eb0_.log
2025-03-14T05:37:39.4241525Z Running 1 items in this shard: test/dynamo/test_resume.py::ResumeFunctionTests::test_freevars
2025-03-14T05:37:39.4242180Z 
2025-03-14T05:37:43.0435502Z Running dynamo/test_export 1/1 ... [2025-03-14 05:37:43.042960]
2025-03-14T05:37:43.0436557Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:37:43.0439242Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:37:43.043293]
2025-03-14T05:37:47.8685255Z 
2025-03-14T05:37:47.8686344Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_37afcec3d972fef7_.log
2025-03-14T05:37:47.8707085Z Running 48 items in this shard: test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_LSTM, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_alias_inputs, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_expand_mutation_backwards, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_expand_mutation_error, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_expand_mutation_functionalizes, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_raises_invalid_leaf_set, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_export_joint_simple_repro, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_grad_mode_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_sequence_nr, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args_param, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args_param_non_tensor_arg, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args_param_non_tensor_arg_list, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_with_global, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_autograd_function_tangent_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_call_fn_with_non_const_inputs_aot_safe, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_call_fn_with_non_const_inputs_aot_unsafe, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_call_fn_with_non_const_inputs_aot_unsafe_control_flow, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_data_ptr_access_copy, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_data_ptr_access_fails_in_backward, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_data_ptr_access_fails_in_forward, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_different_inputs_overlapping_set_with_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer1, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer2, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer3, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer4, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer5, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer6, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph1, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph2, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph3, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph4, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_double_backward_errors, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_eager_sequence_nr, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_grad_inputs_alias_inputs, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_inputs_overlapping_with_mutation_recompile, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_inputs_overlapping_with_mutation_stress, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_multiple_aot_autograd_calls_dupe_args, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_mutation1, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_negative_testing, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_negative_testing_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_nn_parameter_construction, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_no_storage_overlap_guards_no_aliasing, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_no_storage_overlap_guards_no_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_requires_grad_fake_via_dynamo_recompiles, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_split_with_sizes_aot_autograd_cleans_up_traceback_meta
2025-03-14T05:37:47.8727189Z 
2025-03-14T05:37:51.4517770Z Running dynamo/test_recompile_ux 1/1 ... [2025-03-14 05:37:51.451441]
2025-03-14T05:37:51.4518321Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:37:51.4519824Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:37:51.451771]
2025-03-14T05:37:55.8225648Z 
2025-03-14T05:37:55.8227369Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_e1d0b37f56ad5a41_.log
2025-03-14T05:37:55.8233928Z Running 10 items in this shard: test/dynamo/test_recompile_ux.py::RecompileUxTests::test_drop_cache_on_skip, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_dynamic_input, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_fail_on_recompile_limit_hit, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_loop_torture, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_mismatched_type, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_multiple_guard_fails, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_multiple_guard_fails_report_all, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_nvfuser_guards, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_recompile_child_run_only, test/dynamo/test_recompile_ux.py::RecompileUxTests::test_verbose_tensor_check
2025-03-14T05:37:55.8239780Z 
2025-03-14T05:37:59.5562156Z Running dynamo/test_unspec 1/1 ... [2025-03-14 05:37:59.555742]
2025-03-14T05:37:59.5563020Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:37:59.5566959Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:37:59.556125]
2025-03-14T05:38:04.3881644Z 
2025-03-14T05:38:04.3882816Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_f0242e04ceb9fa60_.log
2025-03-14T05:38:04.3945411Z Running 182 items in this shard: test/dynamo/test_export.py::ExportTests::test_access_class_method_from_user_class_attr, test/dynamo/test_export.py::ExportTests::test_access_class_method_from_user_class_builtin, test/dynamo/test_export.py::ExportTests::test_byte_tensor_does_not_crash, test/dynamo/test_export.py::ExportTests::test_capture_symbolic_tracing_simple_within_fake_mode, test/dynamo/test_export.py::ExportTests::test_capture_symbolic_tracing_within_fake_mode, test/dynamo/test_export.py::ExportTests::test_cond_free_variables_overlapping, test/dynamo/test_export.py::ExportTests::test_cond_op_param_buffer_lifted, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_branch_args_mismatch, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_branch_return_multiple_tensors, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_branch_return_non_tensor, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_mismatch_return_length, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_mismatch_return_tensor_meta, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_missing_args, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_non_list_operands, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_non_tensor_operands, test/dynamo/test_export.py::ExportTests::test_cond_raise_user_error_on_unsupported_pred, test/dynamo/test_export.py::ExportTests::test_cond_supported_pred_types, test/dynamo/test_export.py::ExportTests::test_constraint_violation_error_messages, test/dynamo/test_export.py::ExportTests::test_dataclass_input_output, test/dynamo/test_export.py::ExportTests::test_dict_return, test/dynamo/test_export.py::ExportTests::test_dict_return_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_dupes, test/dynamo/test_export.py::ExportTests::test_dupes_2, test/dynamo/test_export.py::ExportTests::test_dupes_2_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass_reorder_with_non_tensor_arg, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass_reorder_with_non_tensor_arg_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass_with_non_tensor_arg, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass_with_non_tensor_arg_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass_with_non_tensor_output, test/dynamo/test_export.py::ExportTests::test_dupes_and_bypass_with_non_tensor_output_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_dupes_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_dynamic_slicing, test/dynamo/test_export.py::ExportTests::test_dynamic_slicing_invalid, test/dynamo/test_export.py::ExportTests::test_dynamic_slicing_simple, test/dynamo/test_export.py::ExportTests::test_dynamo_enum_in_tuple, test/dynamo/test_export.py::ExportTests::test_dynamo_list_index, test/dynamo/test_export.py::ExportTests::test_empty, test/dynamo/test_export.py::ExportTests::test_enforce_equalities, test/dynamo/test_export.py::ExportTests::test_export, test/dynamo/test_export.py::ExportTests::test_export_compare_optimize_with_make_fx, test/dynamo/test_export.py::ExportTests::test_export_cond_in_aten_symbolic, test/dynamo/test_export.py::ExportTests::test_export_control_flow_with_getattr, test/dynamo/test_export.py::ExportTests::test_export_decomp, test/dynamo/test_export.py::ExportTests::test_export_decomp_asserts_bad_args, test/dynamo/test_export.py::ExportTests::test_export_defaults_ok, test/dynamo/test_export.py::ExportTests::test_export_dynamic_control_flow_error, test/dynamo/test_export.py::ExportTests::test_export_dynamic_dim_cleanup, test/dynamo/test_export.py::ExportTests::test_export_dynamic_dim_not_1, test/dynamo/test_export.py::ExportTests::test_export_dynamic_dim_range_constraint, test/dynamo/test_export.py::ExportTests::test_export_graph_bypass, test/dynamo/test_export.py::ExportTests::test_export_graph_bypass_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_export_graph_with_complex_reorder, test/dynamo/test_export.py::ExportTests::test_export_graph_with_complex_reorder_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_export_graph_with_list, test/dynamo/test_export.py::ExportTests::test_export_graph_with_list_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_export_identity, test/dynamo/test_export.py::ExportTests::test_export_masking_with_no_grad, test/dynamo/test_export.py::ExportTests::test_export_meta, test/dynamo/test_export.py::ExportTests::test_export_meta_val, test/dynamo/test_export.py::ExportTests::test_export_mismatched_out, test/dynamo/test_export.py::ExportTests::test_export_mismatched_out_2, test/dynamo/test_export.py::ExportTests::test_export_mismatched_out_2_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_export_mismatched_out_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_export_module_specify_constraints_signature, test/dynamo/test_export.py::ExportTests::test_export_multi_dynamic_dim_constraint, test/dynamo/test_export.py::ExportTests::test_export_multi_dynamic_dim_unsafe_relationship, test/dynamo/test_export.py::ExportTests::test_export_nn_module_stack_patched_module, test/dynamo/test_export.py::ExportTests::test_export_no_raise, test/dynamo/test_export.py::ExportTests::test_export_no_tensor_computation_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_export_pass_arg_by_name, test/dynamo/test_export.py::ExportTests::test_export_pass_arg_by_name_star_args, test/dynamo/test_export.py::ExportTests::test_export_persist_assert, test/dynamo/test_export.py::ExportTests::test_export_preserve_constraints_as_metadata_tensor, test/dynamo/test_export.py::ExportTests::test_export_preserves_nn_module_stack_for_get_attr, test/dynamo/test_export.py::ExportTests::test_export_raise_guard_full_constraint, test/dynamo/test_export.py::ExportTests::test_export_raise_guard_partial_constraint, test/dynamo/test_export.py::ExportTests::test_export_raise_on_relationship, test/dynamo/test_export.py::ExportTests::test_export_shape_control_flow_1, test/dynamo/test_export.py::ExportTests::test_export_specialized_int, test/dynamo/test_export.py::ExportTests::test_export_symbolic_shape, test/dynamo/test_export.py::ExportTests::test_export_with_args_and_empty_kwargs, test/dynamo/test_export.py::ExportTests::test_export_with_args_with_default_None, test/dynamo/test_export.py::ExportTests::test_export_with_args_with_default_float, test/dynamo/test_export.py::ExportTests::test_export_with_args_with_default_tensor, test/dynamo/test_export.py::ExportTests::test_export_with_args_with_default_tuple, test/dynamo/test_export.py::ExportTests::test_export_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_export_with_builtin_op_on_assume_constant, test/dynamo/test_export.py::ExportTests::test_export_with_cond_branches_calling_methods, test/dynamo/test_export.py::ExportTests::test_export_with_cond_closure, test/dynamo/test_export.py::ExportTests::test_export_with_cond_dynamic_shape_pred, test/dynamo/test_export.py::ExportTests::test_export_with_cond_with_closed_function, test/dynamo/test_export.py::ExportTests::test_export_with_constant_dict_values, test/dynamo/test_export.py::ExportTests::test_export_with_constant_free_function, test/dynamo/test_export.py::ExportTests::test_export_with_constant_free_function_and_class_method, test/dynamo/test_export.py::ExportTests::test_export_with_constant_free_function_and_class_method_multiarg, test/dynamo/test_export.py::ExportTests::test_export_with_constant_free_function_and_class_method_multiarg_diff, test/dynamo/test_export.py::ExportTests::test_export_with_constant_global_function, test/dynamo/test_export.py::ExportTests::test_export_with_constant_in_unspecialized_nn_module, test/dynamo/test_export.py::ExportTests::test_export_with_constant_list_nonzero, test/dynamo/test_export.py::ExportTests::test_export_with_constant_list_nonzero_free_function, test/dynamo/test_export.py::ExportTests::test_export_with_constant_method_on_module, test/dynamo/test_export.py::ExportTests::test_export_with_constant_method_on_module_invoke_twice, test/dynamo/test_export.py::ExportTests::test_export_with_constant_none_control_flow, test/dynamo/test_export.py::ExportTests::test_export_with_constant_none_control_flow_free_func, test/dynamo/test_export.py::ExportTests::test_export_with_constant_not_none_control_flow, test/dynamo/test_export.py::ExportTests::test_export_with_constant_not_none_control_flow_free_func, test/dynamo/test_export.py::ExportTests::test_export_with_constant_not_none_control_flow_pos, test/dynamo/test_export.py::ExportTests::test_export_with_constant_not_return_const, test/dynamo/test_export.py::ExportTests::test_export_with_constant_tuple_nonzero, test/dynamo/test_export.py::ExportTests::test_export_with_functools_wrapped_fn, test/dynamo/test_export.py::ExportTests::test_export_with_functools_wrapped_method, test/dynamo/test_export.py::ExportTests::test_export_with_kwargs, test/dynamo/test_export.py::ExportTests::test_export_with_kwargs_and_empty_args, test/dynamo/test_export.py::ExportTests::test_export_with_kwargs_with_default_None, test/dynamo/test_export.py::ExportTests::test_export_with_kwargs_with_default_float, test/dynamo/test_export.py::ExportTests::test_export_with_kwargs_with_default_tensor, test/dynamo/test_export.py::ExportTests::test_export_with_kwargs_with_default_tuple, test/dynamo/test_export.py::ExportTests::test_export_with_map_cond, test/dynamo/test_export.py::ExportTests::test_export_with_map_zero_sized_tensor, test/dynamo/test_export.py::ExportTests::test_export_with_map_zero_sized_tensor_suppress_errors, test/dynamo/test_export.py::ExportTests::test_export_with_module_layer, test/dynamo/test_export.py::ExportTests::test_export_with_nonzero_static, test/dynamo/test_export.py::ExportTests::test_export_with_shallow_list_copy_with_side_effects, test/dynamo/test_export.py::ExportTests::test_export_with_shallow_list_copy_wo_side_effects, test/dynamo/test_export.py::ExportTests::test_export_with_stack_trace, test/dynamo/test_export.py::ExportTests::test_export_with_symbool_inputs, test/dynamo/test_export.py::ExportTests::test_export_with_wrapped_fn, test/dynamo/test_export.py::ExportTests::test_exported_graph_serialization, test/dynamo/test_export.py::ExportTests::test_func_return, test/dynamo/test_export.py::ExportTests::test_func_return_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_fx_pytree, test/dynamo/test_export.py::ExportTests::test_input_container_type, test/dynamo/test_export.py::ExportTests::test_invalid_input_global, test/dynamo/test_export.py::ExportTests::test_invalid_input_global_multiple_access, test/dynamo/test_export.py::ExportTests::test_invalid_input_nonlocal, test/dynamo/test_export.py::ExportTests::test_invalid_input_unused_nonlocal_ok, test/dynamo/test_export.py::ExportTests::test_list_contains, test/dynamo/test_export.py::ExportTests::test_list_not_contains, test/dynamo/test_export.py::ExportTests::test_list_unpack, test/dynamo/test_export.py::ExportTests::test_list_unpack_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_map_cond_param_buffer_lifted, test/dynamo/test_export.py::ExportTests::test_mixed_real_and_fake_inputs, test/dynamo/test_export.py::ExportTests::test_multiple_outputs_op_with_evaluator, test/dynamo/test_export.py::ExportTests::test_nested_cond_op_param_buffer_lifted, test/dynamo/test_export.py::ExportTests::test_no_tensor_computation, test/dynamo/test_export.py::ExportTests::test_no_tensor_computation_2, test/dynamo/test_export.py::ExportTests::test_no_tensor_computation_2_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_no_tensor_computation_fail, test/dynamo/test_export.py::ExportTests::test_not_functionalize, test/dynamo/test_export.py::ExportTests::test_param_buffer_safe_from_mutation_recurse, test/dynamo/test_export.py::ExportTests::test_param_buffer_safe_from_mutation_simple, test/dynamo/test_export.py::ExportTests::test_pre_dispatch_simple, test/dynamo/test_export.py::ExportTests::test_predispatch_with_for_out_dtype, test/dynamo/test_export.py::ExportTests::test_predispatch_with_for_out_dtype_nested, test/dynamo/test_export.py::ExportTests::test_predispatch_with_higher_order, test/dynamo/test_export.py::ExportTests::test_predispatch_with_higher_order_nested, test/dynamo/test_export.py::ExportTests::test_preserve_fx_node_metadata, test/dynamo/test_export.py::ExportTests::test_preserve_fx_node_metadata_graph_break, test/dynamo/test_export.py::ExportTests::test_preserve_fx_node_metadata_inline, test/dynamo/test_export.py::ExportTests::test_preserve_fx_node_metadata_recompile, test/dynamo/test_export.py::ExportTests::test_remove_redundant_dynamic_dim_in_error_message, test/dynamo/test_export.py::ExportTests::test_retracibility, test/dynamo/test_export.py::ExportTests::test_retracibility_dict_container_inp_out, test/dynamo/test_export.py::ExportTests::test_retracibility_nested_list_out, test/dynamo/test_export.py::ExportTests::test_round_dynamic_shapes, test/dynamo/test_export.py::ExportTests::test_subclass_parameters, test/dynamo/test_export.py::ExportTests::test_sum_param, test/dynamo/test_export.py::ExportTests::test_sym_contains, test/dynamo/test_export.py::ExportTests::test_symbolic_tracing_within_fake_mode_with_constraints, test/dynamo/test_export.py::ExportTests::test_symbolic_tracing_within_fake_mode_with_constraints_with_parameters, test/dynamo/test_export.py::ExportTests::test_symbool, test/dynamo/test_export.py::ExportTests::test_torch_inference_mode_ctx, test/dynamo/test_export.py::ExportTests::test_trivial_constraint, test/dynamo/test_export.py::ExportTests::test_uncaptured_higher_order_op_error_not_suppresed, test/dynamo/test_export.py::ExportTests::test_untracked_inputs_in_constraints, test/dynamo/test_export.py::ExportTests::test_zeroes_in_and_out_different_shape_on_test, test/dynamo/test_export.py::ExportTests::test_zeroes_in_and_out_different_shape_on_test_with_aten_graph, test/dynamo/test_export.py::ExportTests::test_zeroes_in_new_shape_scalar_out, test/dynamo/test_export.py::ExportTests::test_zeroes_in_new_shape_scalar_out_permute, test/dynamo/test_export.py::ExportTests::test_zeroes_in_new_shape_scalar_out_permute_dupe_and_bypass
2025-03-14T05:38:04.4003876Z 
2025-03-14T05:38:08.0052436Z Running nn/test_lazy_modules 1/1 ... [2025-03-14 05:38:08.004815]
2025-03-14T05:38:08.0053281Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:38:08.0055053Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_lazy_modules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:38:08.005224]
2025-03-14T05:38:12.1255323Z 
2025-03-14T05:38:12.1256408Z nn/test_lazy_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_lazy_modules_1.1_cf8e1d35fc9344aa_.log
2025-03-14T05:38:12.1274614Z Running 58 items in this shard: test/nn/test_lazy_modules.py::TestLazyModules::test_chained_initialization, test/nn/test_lazy_modules.py::TestLazyModules::test_invalid_functions, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm_with_dict_input, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose1d_kwargs, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d_kwargs, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d_kwargs, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transposed1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_forward_hook, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_linear_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_buffer, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_jit_buffer, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_jit_param, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_parameter, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_pre_forward_hook, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_share_memory_buffer, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_share_memory_param, test/nn/test_lazy_modules.py::TestLazyModules::test_linear, test/nn/test_lazy_modules.py::TestLazyModules::test_linear_state, test/nn/test_lazy_modules.py::TestLazyModules::test_materialize_device, test/nn/test_lazy_modules.py::TestLazyModules::test_materialize_dtype, test/nn/test_lazy_modules.py::TestLazyModules::test_optimizer_pass, test/nn/test_lazy_modules.py::TestLazyModules::test_spectral_norm, test/nn/test_lazy_modules.py::TestLazyModules::test_weight_norm
2025-03-14T05:38:12.1292271Z 
2025-03-14T05:38:12.5892360Z 
2025-03-14T05:38:12.5893748Z test_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_1.1_d9e32011666e8381_.log
2025-03-14T05:38:12.6291793Z Running 637 items in this shard: test/test_autograd.py::TestAutograd::test_access_saved_tensor_twice_without_recomputation_works, test/test_autograd.py::TestAutograd::test_accumulate_grad, test/test_autograd.py::TestAutograd::test_accumulate_grad_posthooks_can_observe_tensor_prehook, test/test_autograd.py::TestAutograd::test_accumulate_grad_posthooks_should_not_execute, test/test_autograd.py::TestAutograd::test_accumulate_grad_tensor_reference, test/test_autograd.py::TestAutograd::test_accumulate_grad_with_zero_numel_grad, test/test_autograd.py::TestAutograd::test_anomaly_assign_parent_cleanup, test/test_autograd.py::TestAutograd::test_anomaly_detect_nan, test/test_autograd.py::TestAutograd::test_anomaly_grad_warnings, test/test_autograd.py::TestAutograd::test_anomaly_mode_no_check_nan, test/test_autograd.py::TestAutograd::test_attribute_deletion, test/test_autograd.py::TestAutograd::test_autograd_inplace_view_of_view, test/test_autograd.py::TestAutograd::test_autograd_inplace_views_creation_meta, test/test_autograd.py::TestAutograd::test_autograd_inplace_views_cross_dtype, test/test_autograd.py::TestAutograd::test_autograd_multiple_views_python, test/test_autograd.py::TestAutograd::test_autograd_node_isinstance, test/test_autograd.py::TestAutograd::test_autograd_print_tensor, test/test_autograd.py::TestAutograd::test_autograd_python_custom_function_inplace, test/test_autograd.py::TestAutograd::test_autograd_simple_views_python, test/test_autograd.py::TestAutograd::test_autograd_views_codegen, test/test_autograd.py::TestAutograd::test_backward, test/test_autograd.py::TestAutograd::test_backward_badcalls, test/test_autograd.py::TestAutograd::test_backward_copy, test/test_autograd.py::TestAutograd::test_backward_create_graph_warns, test/test_autograd.py::TestAutograd::test_backward_hook_relative_ordering, test/test_autograd.py::TestAutograd::test_backward_no_grad, 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test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_different_devices_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_different_dtype_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_different_dtype_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_multi_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_multi_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_simple_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_simple_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_unrelated_outputs_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_unrelated_outputs_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_zero_dim_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_zero_dim_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_raises_no_warnings_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_raises_no_warnings_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_create_graph_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_create_graph_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_strict_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_strict_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_no_grad_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_no_grad_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_scalar_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_scalar_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_create_graph_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_create_graph_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_strict_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_strict_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_no_grad_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_no_grad_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_scalar_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_scalar_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_create_graph_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_create_graph_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_strict_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_strict_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_no_grad_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_no_grad_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_scalar_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_scalar_logging_tensor, test/test_autograd.py::TestAutogradLogging::test_logging, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_advanced_indexing_backwards_large_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_advanced_indexing_backwards_memory_format_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_backward_device_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_complex_scalar_backward_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_copy__cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_copy_forward_ad_broadcasting_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_copy_forward_ad_same_layout_copies_grad_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_copy_r_to_c_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_cross_device_reentrant_autograd_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_free_unneeded_tensor_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_grad_assignment_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_gradcheck_input_output_different_device_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_multiple_output_view_of_view_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_backprop_base_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_backprop_view_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_backprop_view_of_view_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_gradcheck_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_makes_base_require_grad_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_modify_base_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_multi_output_safe_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_multi_output_unsafe_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_multiple_outputs_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_non_contig_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_of_multiple_output_view_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_of_view_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_python_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_then_no_grad_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inplace_on_view_undefined_grad_output_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_inputbuffer_add_multidevice_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_min_max_median_backprops_to_all_values_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_mv_grad_stride_0_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_non_differentiable_ops_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_parameter_resize_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_pin_memory_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_pow_real_negative_base_complex_exponent_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_profiler_emit_itt_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_profiler_emit_nvtx_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_pyscalar_conversions_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_reentrant_parent_error_on_cpu_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_requires_grad_factory_cpu_float32, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_requires_grad_factory_cpu_float64, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_resize_version_bump_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_rnn_backward_to_input_but_not_parameters_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_scatter_index_reduce_amin_amax_backprops_to_all_values_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_scatter_index_reduce_prod_gradgrad_error_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_set_requires_grad_only_for_floats_cpu_float32, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_set_requires_grad_only_for_floats_cpu_float64, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_set_requires_grad_only_for_floats_cpu_int16, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_set_requires_grad_only_for_floats_cpu_int32, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_set_requires_grad_only_for_floats_cpu_int64, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_set_requires_grad_only_for_floats_cpu_int8, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_simple_reentrant_cross_device_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_sparse_backward_cpu_complex128, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_sparse_backward_cpu_float64, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_sparse_ctor_getter_backward_cpu_complex128, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_sparse_ctor_getter_backward_cpu_float64, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_sparse_mask_autograd_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_strided_leaf_grad_layout_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_to_r_to_c_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_unused_output_device_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_warning_in_backward_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_where_functional_cpu, test/test_autograd.py::TestAutogradDeviceTypeCPU::test_where_scalar_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_autograd_composite_implicit_and_dispatch_registration_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_autograd_multiple_dispatch_registrations_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_backward_single_threaded_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_backward_tls_stash_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_foward_mode_AD_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_is_retain_graph_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_per_dispatch_key_input_saving_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_set_sequence_nr_cpu, test/test_autograd.py::TestAutogradMultipleDispatchCPU::test_view_copy_cpu
2025-03-14T05:38:12.6668172Z 
2025-03-14T05:38:15.7590649Z Running inductor/test_codecache 1/1 ... [2025-03-14 05:38:15.758637]
2025-03-14T05:38:15.7591506Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:38:15.7593543Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_codecache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:38:15.758964]
2025-03-14T05:38:16.2409426Z Running test_numba_integration 1/1 ... [2025-03-14 05:38:16.240519]
2025-03-14T05:38:16.2410304Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:38:16.2413851Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_numba_integration.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:38:16.241029]
2025-03-14T05:38:19.8738913Z 
2025-03-14T05:38:19.8740122Z test_numba_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_numba_integration_1.1_b6e2c42a63da429b_.log
2025-03-14T05:38:19.8743766Z Running 8 items in this shard: test/test_numba_integration.py::TestNumbaIntegration::test_active_device, test/test_numba_integration.py::TestNumbaIntegration::test_array_adaptor, test/test_numba_integration.py::TestNumbaIntegration::test_conversion_errors, test/test_numba_integration.py::TestNumbaIntegration::test_cuda_array_interface, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface_active_device, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface_inferred_strides, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface_lifetime
2025-03-14T05:38:19.8747083Z 
2025-03-14T05:38:20.8041337Z 
2025-03-14T05:38:20.8042320Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_3af0baa49d5b01ff_.log
2025-03-14T05:38:20.8056550Z Running 46 items in this shard: test/dynamo/test_unspec.py::UnspecTests::test_argmin_coerces_symint_to_intlist_spec, test/dynamo/test_unspec.py::UnspecTests::test_bool_tensor_ctor, test/dynamo/test_unspec.py::UnspecTests::test_builtin_getitem, test/dynamo/test_unspec.py::UnspecTests::test_builtin_max_min, test/dynamo/test_unspec.py::UnspecTests::test_compiled_random_calls_are_random, test/dynamo/test_unspec.py::UnspecTests::test_conv1d_symint_padding, test/dynamo/test_unspec.py::UnspecTests::test_data_dependent_evaluate_expr_graph_break, test/dynamo/test_unspec.py::UnspecTests::test_defaults, test/dynamo/test_unspec.py::UnspecTests::test_exponential, test/dynamo/test_unspec.py::UnspecTests::test_feed_random_values_into_graph_only, test/dynamo/test_unspec.py::UnspecTests::test_isinstance_symint, test/dynamo/test_unspec.py::UnspecTests::test_item_max, test/dynamo/test_unspec.py::UnspecTests::test_mark_01_dynamic, test/dynamo/test_unspec.py::UnspecTests::test_mark_static_inside, test/dynamo/test_unspec.py::UnspecTests::test_mark_unbacked, test/dynamo/test_unspec.py::UnspecTests::test_mark_unbacked_channels_last, test/dynamo/test_unspec.py::UnspecTests::test_mark_unbacked_hint_consistency, test/dynamo/test_unspec.py::UnspecTests::test_multiple_consecutive_random_calls_before_graph, test/dynamo/test_unspec.py::UnspecTests::test_no_recompilations, test/dynamo/test_unspec.py::UnspecTests::test_no_recompiles, test/dynamo/test_unspec.py::UnspecTests::test_no_recompiles_prod_backward, test/dynamo/test_unspec.py::UnspecTests::test_numpy_correctness, test/dynamo/test_unspec.py::UnspecTests::test_propagate_dynamic_dim, test/dynamo/test_unspec.py::UnspecTests::test_prune_torch_check, test/dynamo/test_unspec.py::UnspecTests::test_random_call_with_while_loop, test/dynamo/test_unspec.py::UnspecTests::test_random_object, test/dynamo/test_unspec.py::UnspecTests::test_random_object_methods, test/dynamo/test_unspec.py::UnspecTests::test_random_object_overriden_methods, test/dynamo/test_unspec.py::UnspecTests::test_random_values_with_graph_break, test/dynamo/test_unspec.py::UnspecTests::test_rshift_dynamic, test/dynamo/test_unspec.py::UnspecTests::test_shape_graph_break, test/dynamo/test_unspec.py::UnspecTests::test_specializing_numpy_float_in_control_flow, test/dynamo/test_unspec.py::UnspecTests::test_split_aot_autograd, test/dynamo/test_unspec.py::UnspecTests::test_sum_dimlist_spec, test/dynamo/test_unspec.py::UnspecTests::test_sym_int_conversion, test/dynamo/test_unspec.py::UnspecTests::test_symbol_guard_limit_before_specialize, test/dynamo/test_unspec.py::UnspecTests::test_symfloat_no_replacement, test/dynamo/test_unspec.py::UnspecTests::test_symfloat_to_tensor, test/dynamo/test_unspec.py::UnspecTests::test_to_tensor, test/dynamo/test_unspec.py::UnspecTests::test_unspec_float_input, test/dynamo/test_unspec.py::UnspecTests::test_unspec_float_input_f64, test/dynamo/test_unspec.py::UnspecTests::test_unspec_float_output, test/dynamo/test_unspec.py::UnspecTests::test_unspec_float_precision, test/dynamo/test_unspec.py::UnspecTests::test_unspec_roundtrip_float_input, test/dynamo/test_unspec.py::UnspecTests::test_unspecialized_float_multiply_precision, test/dynamo/test_unspec.py::UnspecTests::test_use_and_specialize
2025-03-14T05:38:20.8069855Z 
2025-03-14T05:38:23.4892533Z Running dynamo/test_logging 1/1 ... [2025-03-14 05:38:23.488850]
2025-03-14T05:38:23.4893398Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:38:23.4895548Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:38:23.489190]
2025-03-14T05:38:24.4530330Z Running test_decomp 1/9 ... [2025-03-14 05:38:24.452685]
2025-03-14T05:38:24.4530834Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:38:24.4532350Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=1', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:38:24.453026]
2025-03-14T05:38:53.5469800Z 
2025-03-14T05:38:53.5471419Z dynamo/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_logging_1.1_11985e9a3cd0deba_.log
2025-03-14T05:38:53.5486455Z Running 49 items in this shard: test/dynamo/test_logging.py::LoggingTests::test_all, test/dynamo/test_logging.py::LoggingTests::test_aot, test/dynamo/test_logging.py::LoggingTests::test_aot_graphs, test/dynamo/test_logging.py::LoggingTests::test_aot_joint_graph, test/dynamo/test_logging.py::LoggingTests::test_autotuning, test/dynamo/test_logging.py::LoggingTests::test_bytecode, test/dynamo/test_logging.py::LoggingTests::test_cudagraph_static_inputs, test/dynamo/test_logging.py::LoggingTests::test_cudagraphs, test/dynamo/test_logging.py::LoggingTests::test_custom_format, test/dynamo/test_logging.py::LoggingTests::test_custom_format_exc, test/dynamo/test_logging.py::LoggingTests::test_ddp_graphs, test/dynamo/test_logging.py::LoggingTests::test_default_logging, test/dynamo/test_logging.py::LoggingTests::test_distributed_rank_logging, test/dynamo/test_logging.py::LoggingTests::test_dump_compile_times, test/dynamo/test_logging.py::LoggingTests::test_dynamo_debug, test/dynamo/test_logging.py::LoggingTests::test_dynamo_debug_default_off_artifacts, test/dynamo/test_logging.py::LoggingTests::test_dynamo_error, test/dynamo/test_logging.py::LoggingTests::test_dynamo_info, test/dynamo/test_logging.py::LoggingTests::test_fusion, test/dynamo/test_logging.py::LoggingTests::test_graph_breaks, test/dynamo/test_logging.py::LoggingTests::test_graph_region_expansion, test/dynamo/test_logging.py::LoggingTests::test_guards_polyfill_sloc, test/dynamo/test_logging.py::LoggingTests::test_guards_recompiles, test/dynamo/test_logging.py::LoggingTests::test_guards_sloc, test/dynamo/test_logging.py::LoggingTests::test_guards_sloc_vr, test/dynamo/test_logging.py::LoggingTests::test_inductor_debug, test/dynamo/test_logging.py::LoggingTests::test_inductor_error, test/dynamo/test_logging.py::LoggingTests::test_inductor_info, test/dynamo/test_logging.py::LoggingTests::test_invalid_artifact_flag, test/dynamo/test_logging.py::LoggingTests::test_kernel_code, test/dynamo/test_logging.py::LoggingTests::test_log_traced_frames, test/dynamo/test_logging.py::LoggingTests::test_logs_out, test/dynamo/test_logging.py::LoggingTests::test_multiline_format, test/dynamo/test_logging.py::LoggingTests::test_open_registration, test/dynamo/test_logging.py::LoggingTests::test_open_registration_python_api, test/dynamo/test_logging.py::LoggingTests::test_open_registration_with_registered_parent, test/dynamo/test_logging.py::LoggingTests::test_optimizer_non_static_param, test/dynamo/test_logging.py::LoggingTests::test_output_code, test/dynamo/test_logging.py::LoggingTests::test_recompiles, test/dynamo/test_logging.py::LoggingTests::test_schedule, test/dynamo/test_logging.py::LoggingTests::test_trace_call, test/dynamo/test_logging.py::LoggingTests::test_trace_call_graph_break, test/dynamo/test_logging.py::LoggingTests::test_trace_call_inline_call, test/dynamo/test_logging.py::LoggingTests::test_trace_call_prefix, test/dynamo/test_logging.py::LoggingTests::test_trace_source_cond, test/dynamo/test_logging.py::LoggingTests::test_trace_source_funcname, test/dynamo/test_logging.py::LoggingTests::test_trace_source_if_stmt, test/dynamo/test_logging.py::LoggingTests::test_trace_source_nested, test/dynamo/test_logging.py::LoggingTests::test_trace_source_simple
2025-03-14T05:38:53.5500945Z 
2025-03-14T05:38:57.0624597Z Running test_ops_gradients 2/2 ... [2025-03-14 05:38:57.062101]
2025-03-14T05:38:57.0625140Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:38:57.0626851Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_gradients.py', '-m', 'not serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:38:57.062455]
2025-03-14T05:39:22.7150851Z 
2025-03-14T05:39:22.7152970Z inductor/test_codecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_codecache_1.1_ef4ab00c1de979c0_.log
2025-03-14T05:39:22.7255355Z Running 112 items in this shard: test/inductor/test_codecache.py::TestFxGraphCache::test_async_compile_cache, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_clear, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_guard, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_guard_overspec, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_bfloat16_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_bfloat16_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_bfloat16_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_bfloat16_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_pgo, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_repeat, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float64_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float64_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float64_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float64_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_int32_bounds_device_cuda_bfloat16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_int32_bounds_device_cuda_float16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cpu_bfloat16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cpu_float32, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cuda_bfloat16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cuda_float32, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_with_nt, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_with_symint_non_arg_guard, test/inductor/test_codecache.py::TestFxGraphCache::test_constant_handling_device_cpu, test/inductor/test_codecache.py::TestFxGraphCache::test_constant_handling_device_cuda, test/inductor/test_codecache.py::TestFxGraphCache::test_flex_attention_caching, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cpu_inlinable_False, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cpu_inlinable_True, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cuda_inlinable_False, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cuda_inlinable_True, test/inductor/test_codecache.py::TestFxGraphCache::test_generated_kernel_count, test/inductor/test_codecache.py::TestFxGraphCache::test_higher_order_op_bypass_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_higher_order_op_bypass_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_inductor_counters, test/inductor/test_codecache.py::TestFxGraphCache::test_no_arguments_tensor_device_guards, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_tensor_device_guards_cpu_tensor, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_different_configs_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_different_configs_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_op_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_op_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_bypass_unsupported, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_get_hash_for_files, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_config_changes, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_custom_passes, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_fake_tensors, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_kwargs, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_parameter_constants, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_stable_strings, test/inductor/test_codecache.py::TestCudaCompileCommand::test_cuda_compile_command, test/inductor/test_codecache.py::TestAutotuneCache::test_autotune_cache, test/inductor/test_codecache.py::TestAutotuneCache::test_bundled_autotune_remote_cache, test/inductor/test_codecache.py::TestRemoteAOTAutogradCache::test_autograd_remote_cache, test/inductor/test_codecache.py::TestRemoteAOTAutogradCache::test_autograd_remote_lazy_backward, test/inductor/test_codecache.py::TestUtils::test_force_disable_coordinate_descent, test/inductor/test_codecache.py::TestUtils::test_fresh_inductor_cache
2025-03-14T05:39:22.7356262Z 
2025-03-14T05:39:26.4818128Z Running test_type_hints 1/1 ... [2025-03-14 05:39:26.481474]
2025-03-14T05:39:26.4818631Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:39:26.4820976Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_hints.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:39:26.481850]
2025-03-14T05:39:30.0509047Z 
2025-03-14T05:39:30.0510020Z test_type_hints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_hints_1.1_afa668e0a5de7286_.log
2025-03-14T05:39:30.0511000Z Running 1 items in this shard: test/test_type_hints.py::TestTypeHints::test_doc_examples
2025-03-14T05:39:30.0511451Z 
2025-03-14T05:39:33.5880499Z Running lazy/test_extract_compiled_graph 1/1 ... [2025-03-14 05:39:33.587718]
2025-03-14T05:39:33.5881044Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:39:33.5882816Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:39:33.588054]
2025-03-14T05:39:35.1130690Z 
2025-03-14T05:39:35.1132060Z lazy/test_extract_compiled_graph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_extract_compiled_graph_1.1_83af56c8728f507a_.log
2025-03-14T05:39:35.1132865Z 
2025-03-14T05:39:38.6465223Z Running test_meta 1/4 ... [2025-03-14 05:39:38.646157]
2025-03-14T05:39:38.6465753Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:39:38.6467773Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_meta.py', '-m', 'not serial', '--shard-id=1', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:39:38.646476]
2025-03-14T05:45:04.2597982Z 
2025-03-14T05:45:04.2598855Z test_ops_gradients 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_gradients_2.2_6c8396a0917cd15c_.log
2025-03-14T05:45:04.3653405Z Running 2656 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_NumpyCatCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyMulCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpySplitCopyCustomOp_cpu_float64, 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__batch_norm_with_update_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__segment_reduce_offsets_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_put_accumulate_cpu_float64, 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_alias_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_alias_copy_cpu_float64, 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_copy_cpu_float64, 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, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pixel_unshuffle_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pixel_unshuffle_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_poisson_nll_loss_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_prelu_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_relu6_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_relu_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_rms_norm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_rrelu_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_scaled_dot_product_attention_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_normal_in_place_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_normal_number_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_like_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ormqr_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_outer_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_outer_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pca_lowrank_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_permute_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_permute_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_permute_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pinverse_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_3_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_4_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_positive_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_positive_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_put_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_repeat_interleave_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_repeat_interleave_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_as_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize__cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_conj_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_roll_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rot90_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rot90_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_decimals_3_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_decimals_neg_3_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsub_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scalar_tensor_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_add_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_searchsorted_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_select_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sgn_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sgn_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_short_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sign_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_cosine_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_exponential_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_kaiser_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sin_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinc_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinh_cpu_complex128, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_laguerre_polynomial_l_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_log_ndtr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_i1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_k0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_polygamma_special_polygamma_n_0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_scaled_modified_bessel_k1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_t_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_w_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stft_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stft_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sub_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_to_size_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_to_size_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_lowrank_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_copy_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_transpose_copy_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_unbind_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_copy_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_unsqueeze_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_copy_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_copy_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
2025-03-14T05:45:04.4676946Z 
2025-03-14T05:45:08.3945263Z Running test_meta 2/4 ... [2025-03-14 05:45:08.394139]
2025-03-14T05:45:08.3946303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:45:08.3948243Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_meta.py', '-m', 'not serial', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:45:08.394483]
2025-03-14T05:48:13.2713956Z 
2025-03-14T05:48:13.2714824Z test_decomp 1/9 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_1.9_2aba87cdaa50adf7_.log
2025-03-14T05:48:13.3227604Z Running 1035 items in this shard: test/test_decomp.py::TestDecompCPU::test_comprehensive_H_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rpow___cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rpow___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rxor___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive__batch_norm_with_update_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive__chunk_cat_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive__native_batch_norm_legit_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__native_batch_norm_legit_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive__softmax_backward_data_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_cpu_uint8, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_lu_factor_ex_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_matrix_norm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_matrix_rank_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_matrix_rank_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_multi_dot_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_norm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_solve_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_solve_ex_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_solve_triangular_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_svd_cpu_float32, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_binary_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_binary_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_reduction_with_dim_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_minimum_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_mm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_mode_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_movedim_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_movedim_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_movedim_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_movedim_cpu_int64, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_nanmedian_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nansum_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nansum_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_narrow_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_narrow_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_narrow_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_native_batch_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_native_dropout_backward_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_native_layer_norm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_ne_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_neg_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_neg_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_empty_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_empty_strided_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_empty_strided_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_full_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_zeros_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_zeros_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nextafter_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_avg_pool2d_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_conv1d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_conv2d_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_conv3d_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_conv_transpose2d_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_conv_transpose3d_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_cosine_similarity_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_cosine_similarity_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_cross_entropy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_cross_entropy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_ctc_loss_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_dropout2d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_dropout3d_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_elu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_embedding_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_feature_alpha_dropout_with_train_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_hardsigmoid_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_hardswish_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_hardtanh_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_hinge_embedding_loss_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_kl_div_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_layer_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_layer_norm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_leaky_relu_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_linear_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_linear_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_pool1d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_pool2d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_pool3d_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_unpool1d_grad_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_unpool3d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_unpool3d_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_max_unpool3d_grad_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_mish_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_multilabel_margin_loss_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_nll_loss_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_normalize_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pad_constant_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pad_constant_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pad_reflect_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pad_reflect_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pad_replicate_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pairwise_distance_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pdist_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pixel_shuffle_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_pixel_unshuffle_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_poisson_nll_loss_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_relu6_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_relu6_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_relu_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_smooth_l1_loss_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softshrink_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softsign_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_tanhshrink_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_tanhshrink_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_tanhshrink_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_tanhshrink_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_threshold_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_upsample_bilinear_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_upsample_nearest_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nonzero_static_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_norm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_norm_inf_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_norm_inf_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_norm_nuc_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_ones_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_ones_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_ones_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_ones_like_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_ones_like_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_ones_like_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_outer_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_outer_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_outer_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_pca_lowrank_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_permute_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_permute_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_permute_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_pinverse_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_0_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_0_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_positive_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_pow_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_quantile_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_rad2deg_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_rand_like_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_randint_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_randint_like_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_randint_like_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_ravel_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_real_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_real_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_real_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_real_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_reciprocal_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_remainder_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_remainder_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_remainder_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_renorm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_repeat_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_repeat_interleave_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_repeat_interleave_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_reshape_as_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_reshape_as_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_reshape_as_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_reshape_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_reshape_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_resize__cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_resize__cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_resolve_conj_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_resolve_conj_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_resolve_neg_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_resolve_neg_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_rot90_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_round_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_round_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_round_decimals_0_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_round_decimals_0_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_rsqrt_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_rsub_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_rsub_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_scalar_tensor_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_add_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_add_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_amax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_amax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_amin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_mean_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_mean_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_mean_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_prod_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_scatter_reduce_prod_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_searchsorted_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_select_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_sgn_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_short_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_sigmoid_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_sign_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_sign_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_blackman_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_cosine_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_exponential_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_exponential_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_general_cosine_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_general_cosine_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_general_hamming_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_signal_windows_kaiser_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_signbit_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_sin_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinc_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinc_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinc_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinc_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_slice_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_softmax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_airy_ai_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_bessel_j0_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_bessel_j1_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_bessel_y0_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_bessel_y0_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_chebyshev_polynomial_t_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_chebyshev_polynomial_v_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_chebyshev_polynomial_w_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_hermite_polynomial_h_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1e_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_laguerre_polynomial_l_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_log_ndtr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_modified_bessel_i0_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_modified_bessel_k0_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_modified_bessel_k0_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_ndtr_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_ndtr_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_ndtr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_ndtri_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_ndtri_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_scaled_modified_bessel_k1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_xlog1py_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_xlog1py_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_zeta_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_zeta_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_split_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_split_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_split_with_sizes_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_split_with_sizes_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_split_with_sizes_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_split_with_sizes_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_sqrt_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_sqrt_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_squeeze_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_squeeze_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_squeeze_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_squeeze_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_squeeze_multiple_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_stack_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_std_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_std_mean_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_std_mean_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_std_unbiased_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_std_unbiased_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_std_unbiased_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_sum_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_sum_to_size_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_sum_to_size_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_take_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_tan_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_tanh_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_tensor_split_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_tensordot_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_tensordot_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_to_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_to_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_to_sparse_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_topk_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_torch_ops_aten__safe_softmax_default_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapezoid_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapezoid_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapezoid_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapz_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapz_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapz_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_triu_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_triu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_true_divide_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_true_divide_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_trunc_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_trunc_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unbind_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unbind_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_unbind_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_unbind_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_unbind_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unbind_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_unbind_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_unflatten_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_unflatten_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_unflatten_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unfold_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_unique_consecutive_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unique_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unravel_index_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsafe_chunk_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsafe_chunk_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsafe_split_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsqueeze_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_var_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_var_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_var_unbiased_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_vdot_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_as_complex_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_as_complex_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_as_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_vsplit_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_vstack_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_vstack_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_vstack_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_vstack_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_xlogy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_like_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_like_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick__chunk_cat_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick__chunk_cat_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick__native_batch_norm_legit_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick__softmax_backward_data_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_put_accumulate_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_abs_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_abs_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_acosh_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_acosh_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_acosh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_addr_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_amax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_aminmax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_any_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_asin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_atan_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_atanh_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_baddbmm_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_and_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_and_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_left_shift_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_not_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_or_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_block_diag_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_block_diag_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_bucketize_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_ceil_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_min_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_clone_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_clone_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_conj_physical_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_conj_physical_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_constant_pad_nd_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_copysign_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_copysign_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_fill_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_native_dropout_backward_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nn_functional_hardshrink_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_norm_fro_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_sgn_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_special_xlog1py_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_t_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_transpose_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_dist_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_dist_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_div_trunc_rounding_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_eq_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_erf_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_exp_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_exp_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_expand_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_expm1_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_exponential_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfftn_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfftn_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft2_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfftn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_floor_divide_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fmax_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_frac_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_frexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_ge_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_geometric_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_grid_sampler_2d_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_gt_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_gt_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_heaviside_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_hypot_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_isin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_isin_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_isposinf_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_isposinf_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_item_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_linalg_vector_norm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_log10_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_log2_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_log2_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_log2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_log_normal_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_log_normal_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_log_softmax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_logaddexp2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_logit_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_logspace_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_logsumexp_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_maximum_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_list_of_tensors_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_minimum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_nansum_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_ne_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_ne_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_strided_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_strided_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_strided_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_nextafter_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_embedding_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_gelu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_mish_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_mish_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_rrelu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_rrelu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_softplus_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_softshrink_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_norm_nuc_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_normal_number_mean_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_ones_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_ones_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_rad2deg_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_rad2deg_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_reciprocal_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_renorm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_renorm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_select_scatter_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_special_erfcx_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_special_erfcx_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_i1e_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_log_ndtr_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtr_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_zeta_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_zeta_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_split_list_args_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_split_list_args_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_std_mean_unbiased_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_std_unbiased_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sub_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_sub_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_trace_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_unbind_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_unbind_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_unbind_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_unbind_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_unfold_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_unfold_copy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_uniform_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_uniform_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_var_mean_unbiased_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_var_mean_unbiased_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_var_unbiased_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_var_unbiased_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_view_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_view_copy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_zeros_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_zeros_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_int64, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_LSTM_eval_mode_cpu_float32, test/test_decomp.py::TestDecompCPU::test_uniform_cpu
2025-03-14T05:48:13.3833867Z 
2025-03-14T05:48:16.8599926Z Running optim/test_optim 1/1 ... [2025-03-14 05:48:16.859626]
2025-03-14T05:48:16.8600407Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:48:16.8601917Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:48:16.859970]
2025-03-14T05:48:20.1908387Z 
2025-03-14T05:48:20.1909344Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_711f242505901bc2_.log
2025-03-14T05:48:20.1910041Z 
2025-03-14T05:48:23.8634679Z Running test_ops 3/6 ... [2025-03-14 05:48:23.863114]
2025-03-14T05:48:23.8635181Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:48:23.8637097Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:48:23.863464]
2025-03-14T05:48:31.8654228Z 
2025-03-14T05:48:31.8655048Z test_meta 1/4 was successful, full logs can be found in artifacts with path test/test-reports/test_meta_1.4_b0271ba4140c778b_.log
2025-03-14T05:48:32.2134033Z Running 10168 items in this shard: test/test_meta.py::TestMetaConverter::test_complex_noncontiguous_bug, test/test_meta.py::TestMetaConverter::test_inplace_set_storage, test/test_meta.py::TestMetaConverter::test_leaf, test/test_meta.py::TestMetaConverter::test_non_leaf, test/test_meta.py::TestMetaConverter::test_view_of_leaf, test/test_meta.py::TestMetaConverter::test_weakref, test/test_meta.py::TestMetaCPU::test_batch_norm_backward_output_mask1_cpu, test/test_meta.py::TestMetaCPU::test_batch_norm_backward_output_mask2_cpu, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___radd___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___rpow___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_clamp_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_copysign_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_div_no_rounding_mode_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_eq_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_gt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_heaviside_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_igamma_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_logical_or_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_logical_xor_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_lt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_special_xlog1py_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_special_zeta_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_eq_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_heaviside_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_igamma_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_isclose_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_le_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_logaddexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_lt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_min_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_hermite_polynomial_he_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_xlog1py_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rdiv___cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rdiv___cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rdiv___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmul___cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmul___cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmul___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___ror___cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___ror___cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___ror___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rpow___cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rpow___cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rpow___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rxor___cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rxor___cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__batch_norm_with_update_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__chunk_cat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__chunk_cat_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_abs_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_abs_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_abs_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_acos_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_acos_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_acos_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_acos_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_acos_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcdiv_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcdiv_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcdiv_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcdiv_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcmul_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcmul_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcmul_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcmul_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcmul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_atan_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_atan_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_atan_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_ceil_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_ceil_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_max_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_max_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_min_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cosh_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cosh_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cosh_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_div_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_div_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_div_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_div_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_exp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_floor_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_floor_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_floor_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_floor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_frac_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_frac_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_frac_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_frac_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_frac_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_frac_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log10_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log10_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log10_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log2_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log2_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_max_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_max_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_max_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_maximum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_maximum_cpu_bool, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acos_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acos_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acosh_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acosh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_add_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_add_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_allclose_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amax_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amax_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amin_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amin_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_aminmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_aminmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_angle_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_angle_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_angle_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_arange_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_arange_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmin_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmin_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argsort_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_2d_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_2d_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_3d_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_3d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cat_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cdouble_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cdouble_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cdouble_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_ceil_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_ceil_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cfloat_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cfloat_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chalf_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chalf_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chalf_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_char_cpu_float32, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_max_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_min_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_min_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_min_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_min_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_min_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cumulative_trapezoid_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cumulative_trapezoid_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cumulative_trapezoid_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_deg2rad_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_deg2rad_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_deg2rad_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_deg2rad_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_diag_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_diag_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_diag_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_diag_embed_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_diag_embed_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erf_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfc_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfc_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfc_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfc_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfinv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfinv_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfinv_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfinv_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_erfinv_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_exp2_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_exp2_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_exp_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_fft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_fft_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_fft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_fftshift_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_fftshift_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_fftshift_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_fftshift_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_hfft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_hfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_hfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_hfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifft_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifft_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifft_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifftshift_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ifftshift_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ihfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ihfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ihfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ihfft_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ihfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_ihfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_irfft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_irfft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_irfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_irfft_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_irfftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_irfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_rfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_rfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_rfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fft_rfftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fill_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fill_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fill_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_flatten_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_flatten_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_flatten_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_flatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_flip_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_flip_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_fliplr_cpu_bool, 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test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_trunc_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unbind_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unflatten_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unflatten_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unflatten_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unflatten_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unflatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unfold_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unfold_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unfold_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unfold_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unfold_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unfold_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unfold_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unique_consecutive_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unique_consecutive_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unique_consecutive_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unique_cpu_uint16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unique_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unravel_index_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unravel_index_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unravel_index_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsafe_chunk_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsafe_split_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsqueeze_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsqueeze_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsqueeze_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsqueeze_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsqueeze_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_unsqueeze_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_var_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_var_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_var_mean_unbiased_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_var_mean_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_var_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_var_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vdot_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vdot_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_as_complex_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_as_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_as_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_as_real_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_view_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vsplit_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vsplit_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vsplit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vstack_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vstack_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vstack_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vstack_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_vstack_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_where_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_where_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_xlogy_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_xlogy_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_symbolic_meta_outplace_zero__cpu_bool, 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test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_clamp_min_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cos_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cos_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cos_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cos_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cosh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cosh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_div_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_div_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_div_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erf_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erfc_cpu_bool, 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test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sigmoid_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sigmoid_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sigmoid_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sub_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_trunc_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_trunc_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_trunc_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_trunc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__native_batch_norm_legit_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_lengths_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_offsets_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_offsets_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__softmax_backward_data_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__upsample_bilinear2d_aa_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__upsample_bilinear2d_aa_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcdiv_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcmul_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_decomposed_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_decomposed_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_amax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_any_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_any_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_any_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_arange_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_arange_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_arange_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argsort_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_argsort_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_argsort_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argsort_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan2_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_atanh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_atanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_2d_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_2d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_2d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_baddbmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_baddbmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bincount_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_and_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_and_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_and_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_or_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_right_shift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_right_shift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_shapes_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bucketize_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bucketize_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bucketize_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bucketize_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bucketize_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_char_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_char_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_char_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_char_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_inverse_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_solve_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_chunk_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_chunk_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_chunk_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_max_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_min_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_min_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_min_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_min_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_column_stack_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_column_stack_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_column_stack_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_combinations_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_combinations_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_complex_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_physical_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_physical_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_physical_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_physical_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_constant_pad_nd_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_constant_pad_nd_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_constant_pad_nd_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cosh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cosh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_count_nonzero_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_count_nonzero_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_count_nonzero_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_count_nonzero_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cov_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cov_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cov_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cross_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cross_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cross_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumprod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumprod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumsum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumsum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumsum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumulative_trapezoid_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumulative_trapezoid_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumulative_trapezoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumulative_trapezoid_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_deg2rad_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_deg2rad_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_deg2rad_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_deg2rad_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_deg2rad_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diff_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_digamma_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_digamma_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_dist_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dist_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_floor_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_floor_rounding_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_floor_rounding_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_trunc_rounding_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_trunc_rounding_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_trunc_rounding_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_dot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dot_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dot_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_double_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_dsplit_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_einsum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_einsum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_einsum_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_like_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_like_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_like_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_strided_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_eq_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_eq_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_eq_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_equal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_equal_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_equal_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_erf_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_erf_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_erf_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_erf_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_erfc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_erfc_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_erfc_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_erfc_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_erfinv_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_as_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_expm1_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_expm1_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_expm1_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_exponential_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_float8_e5m2, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftshift_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftshift_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftshift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fmin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fmin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fmod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fmod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_frac_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_frexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_frexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_frexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_full_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_full_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_full_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_full_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_full_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_gather_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_gather_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_gcd_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_gcd_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_gcd_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ge_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_ge_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_geometric_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_geometric_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_geometric_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_grid_sampler_2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_gt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_gt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_half_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_half_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_half_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_heaviside_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_heaviside_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_heaviside_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_heaviside_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_histc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_histogram_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_hsplit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_hsplit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_hsplit_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_hsplit_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_hsplit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_hstack_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_hstack_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_igamma_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_imag_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_add_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_add_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_add_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_fill_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_fill_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_put_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_put_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_put_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_put_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_put_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_amax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_amax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_amin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_mean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_mean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_mean_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_mean_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_prod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_prod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_reduce_prod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_select_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_select_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_select_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_select_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_index_select_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_inner_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_inner_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_inner_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_inner_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_int_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_int_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_int_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_int_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_isclose_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isclose_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_isclose_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_isfinite_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_isfinite_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_isfinite_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_isin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_isin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_isinf_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_isnan_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isnan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_isneginf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_isneginf_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isneginf_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_isneginf_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isneginf_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_isneginf_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_isposinf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_isposinf_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isposinf_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_isposinf_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_isreal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_isreal_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_isreal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_isreal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_item_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_item_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_2inputs_2outputs_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_2inputs_2outputs_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_2inputs_2outputs_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_2inputs_2outputs_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_4inputs_with_extra_args_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_binary_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_binary_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_binary_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_binary_return_by_ref_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_binary_return_by_ref_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_unary_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_jiterator_unary_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_kron_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_kthvalue_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lcm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ldexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ldexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_le_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_le_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_le_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_le_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_lgamma_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lgamma_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lgamma_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_cond_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_cond_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_cond_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_cross_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_cross_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_cross_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_diagonal_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_diagonal_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_eig_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_eig_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_eig_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_eigh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_eigh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_eigvals_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_eigvalsh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_householder_product_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_householder_product_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_inv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_inv_ex_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_inv_ex_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_ldl_factor_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_ldl_factor_ex_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_ldl_factor_ex_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_ldl_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_lstsq_grad_oriented_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_lu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_lu_factor_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_lu_factor_ex_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_lu_solve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_norm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_power_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_rank_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_rank_hermitian_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_multi_dot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_multi_dot_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_multi_dot_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_multi_dot_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_multi_dot_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_multi_dot_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_pinv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_pinv_hermitian_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_slogdet_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_solve_ex_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_solve_ex_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_solve_triangular_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_solve_triangular_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svd_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorinv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorinv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorsolve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorsolve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vecdot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vector_norm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vector_norm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vector_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_tensor_overload_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_tensor_overload_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_tensor_overload_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_tensor_overload_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_tensor_overload_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_tensor_overload_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_linspace_tensor_overload_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_log10_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log10_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_log1p_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log1p_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_log1p_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_log2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_log2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_normal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logaddexp2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logaddexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_and_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_and_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_and_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_and_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_and_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_not_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_not_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_not_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_not_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_not_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_or_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_or_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_tensor_overload_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_tensor_overload_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_tensor_overload_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_unpack_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_log_softmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_log_softmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_median_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_matmul_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_matrix_exp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_binary_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_binary_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_no_dim_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_no_dim_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_no_dim_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_maximum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mean_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_list_of_tensors_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_list_of_tensors_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_list_of_tensors_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_list_of_tensors_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_list_of_tensors_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_variadic_tensors_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_binary_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_reduction_no_dim_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmean_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_bool, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_elu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_embedding_bag_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_embedding_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_feature_alpha_dropout_without_train_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_fractional_max_pool2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_fractional_max_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_gaussian_nll_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_glu_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_glu_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_grid_sample_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_group_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_hardshrink_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_hardsigmoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_hardtanh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_huber_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_huber_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_instance_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_area_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_bicubic_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_bilinear_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_linear_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_linear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_nearest-exact_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_nearest_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_nearest_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_layer_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_layer_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_leaky_relu_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_leaky_relu_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_linear_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_linear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_linear_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_local_response_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_local_response_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_margin_ranking_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_margin_ranking_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_margin_ranking_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_margin_ranking_loss_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool2d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool3d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool3d_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool3d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_pool3d_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_unpool1d_grad_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_unpool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_unpool2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_unpool2d_grad_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_unpool3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_unpool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_max_unpool3d_grad_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_mish_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_multi_head_attention_forward_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_multi_margin_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_multi_margin_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_nll_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_circular_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_circular_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_constant_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_reflect_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_replicate_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_replicate_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_replicate_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pad_replicate_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pairwise_distance_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pairwise_distance_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pairwise_distance_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pdist_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pixel_shuffle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pixel_shuffle_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pixel_unshuffle_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pixel_unshuffle_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_pixel_unshuffle_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_soft_margin_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softmin_with_dtype_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softmin_with_dtype_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softplus_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softshrink_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softshrink_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softsign_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softsign_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_softsign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_tanhshrink_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_tanhshrink_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_normal_number_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ones_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ones_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ones_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ones_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ones_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ones_like_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ones_like_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_pca_lowrank_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_add_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_sgn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_short_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_sigmoid_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_sigmoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sigmoid_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sigmoid_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_sign_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_sign_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_bartlett_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_blackman_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_exponential_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_gaussian_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_general_cosine_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_general_cosine_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_general_hamming_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_signal_windows_hann_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_signbit_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_signbit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_sin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sinh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sinh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_sinh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_slice_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_slice_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_slice_scatter_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i0e_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i0e_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i0e_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i1_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i1e_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i1e_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i1e_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_laguerre_polynomial_l_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_laguerre_polynomial_l_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_laguerre_polynomial_l_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_laguerre_polynomial_l_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_laguerre_polynomial_l_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_legendre_polynomial_p_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_legendre_polynomial_p_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_log_ndtr_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_i0_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_i0_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_i0_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_i1_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_i1_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_i1_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_k0_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_k0_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_k1_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_k1_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_k1_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_ndtr_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_ndtri_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_ndtri_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_polygamma_special_polygamma_n_0_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_polygamma_special_polygamma_n_0_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_scaled_modified_bessel_k0_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_shifted_chebyshev_polynomial_w_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_spherical_bessel_j0_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_spherical_bessel_j0_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_spherical_bessel_j0_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_xlog1py_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_zeta_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_list_args_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_list_args_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_list_args_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_list_args_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unravel_index_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vstack_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vstack_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vstack_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vstack_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vstack_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_where_cpu_int64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_bernoulli_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bernoulli_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bernoulli_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_xor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bool_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bool_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_to_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_to_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_to_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_to_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bucketize_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bucketize_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cat_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cauchy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdist_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdouble_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdouble_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdouble_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdouble_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_ceil_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ceil_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cfloat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_cfloat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_chalf_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_char_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_char_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cholesky_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cholesky_inverse_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cholesky_inverse_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cholesky_solve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_cholesky_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_chunk_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_chunk_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_chunk_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_chunk_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_max_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_max_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_min_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_min_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_clone_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_clone_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_combinations_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_contiguous_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_contiguous_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_copysign_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_copysign_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_corrcoef_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cos_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_cos_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cosh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cosh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_count_nonzero_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_count_nonzero_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_count_nonzero_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_count_nonzero_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cov_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_cov_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cross_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cross_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cross_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cross_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummax_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumprod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumprod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumprod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumprod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumsum_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumsum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_deg2rad_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_deg2rad_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagflat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_scatter_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_diff_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_digamma_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_dist_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_trunc_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_trunc_rounding_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_trunc_rounding_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_trunc_rounding_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_dot_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_permuted_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_permuted_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_permuted_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_permuted_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_erf_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_erf_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfc_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfc_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_as_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_expm1_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_expm1_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_eye_cpu_float8_e4m3fnuz, test/test_meta.py::TestMetaCPU::test_meta_outplace_eye_cpu_float8_e5m2fnuz, test/test_meta.py::TestMetaCPU::test_meta_outplace_eye_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fft2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft2_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfftn_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfft2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fill_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fill_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fill_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fill_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fill_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fliplr_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fliplr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fliplr_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fliplr_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_flipud_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_flipud_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_flipud_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_flipud_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_flipud_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_divide_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fmin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fmod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_frexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_like_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_like_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_like_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_like_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_like_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_full_like_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_gather_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_gather_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_gather_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_gather_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_ge_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ge_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_geometric_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_geometric_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_geometric_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_geometric_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_gradient_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_gradient_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_gradient_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_gradient_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_grid_sampler_2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_gt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_gt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_gt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_half_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_half_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_half_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_heaviside_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_heaviside_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_hsplit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_hsplit_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_hsplit_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_hstack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_hstack_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_hstack_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_hstack_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_i0_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_i0_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_i0_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_i0_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_index_add_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_inner_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_int_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_int_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_int_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_int_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_int_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_isclose_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_isfinite_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_isfinite_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_isfinite_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_isfinite_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_isin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_isinf_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_isinf_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_isinf_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_isnan_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_isnan_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_isnan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_isneginf_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_isneginf_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_isneginf_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_isneginf_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_isposinf_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_isposinf_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_isreal_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_istft_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_item_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_item_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_2inputs_2outputs_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_2inputs_2outputs_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_2inputs_2outputs_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_4inputs_with_extra_args_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_4inputs_with_extra_args_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_4inputs_with_extra_args_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_binary_return_by_ref_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_unary_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_jiterator_unary_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_kron_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_kron_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_kron_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_kthvalue_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_kthvalue_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ldexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ldexp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ldexp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_le_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_le_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_lerp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_lgamma_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_lgamma_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lgamma_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_lgamma_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lgamma_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cholesky_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cond_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cond_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cross_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cross_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cross_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cross_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cross_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cross_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_cross_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_det_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_diagonal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_diagonal_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_diagonal_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_eig_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_eigh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_eigvals_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_eigvalsh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_eigvalsh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_householder_product_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_inv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_inv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_inv_ex_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_inv_ex_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_ldl_factor_ex_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_lstsq_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_lu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_lu_factor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_lu_factor_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_lu_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_lu_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_norm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_norm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_power_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_rank_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_rank_hermitian_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_matrix_rank_hermitian_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_multi_dot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_multi_dot_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_multi_dot_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_multi_dot_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_multi_dot_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_norm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_norm_subgradients_at_zero_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_pinv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_pinv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_pinv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_pinv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_pinv_hermitian_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_pinv_hermitian_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_pinv_singular_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_qr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_solve_triangular_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_solve_triangular_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_svd_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_svdvals_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_svdvals_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_tensorinv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_tensorinv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_vander_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_vecdot_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_vecdot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_vecdot_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_vector_norm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_linalg_vector_norm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linspace_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linspace_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_linspace_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_linspace_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linspace_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_linspace_tensor_overload_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_linspace_tensor_overload_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_log10_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_log10_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_log10_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_log10_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_log10_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_log1p_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_log1p_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_log1p_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_log2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_log2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_log2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_normal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_softmax_with_dtype_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_softmax_with_dtype_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_softmax_with_dtype_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_softmax_with_dtype_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_log_softmax_with_dtype_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logaddexp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logcumsumexp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logcumsumexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logcumsumexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logcumsumexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_log_softmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_logsumexp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_mean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_mean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_median_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_normalize_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_prod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_prod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_scatter_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_select_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_select_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_select_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_select_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_select_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_softmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_softmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_softmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_std_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_std_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_sum_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_sum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_var_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_var_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_var_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_matmul_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_matmul_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_matrix_exp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_max_binary_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_max_binary_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_max_pool2d_with_indices_backward_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_max_reduction_no_dim_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_max_reduction_no_dim_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_max_reduction_with_dim_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_maximum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_maximum_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_maximum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_maximum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_maximum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mean_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_meshgrid_list_of_tensors_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_meshgrid_list_of_tensors_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_meshgrid_list_of_tensors_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_meshgrid_variadic_tensors_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_meshgrid_variadic_tensors_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_binary_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_binary_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_binary_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_binary_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_reduction_no_dim_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_reduction_no_dim_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_reduction_no_dim_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_reduction_with_dim_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_min_reduction_with_dim_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_minimum_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_minimum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_minimum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_minimum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_minimum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_minimum_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_mm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mode_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mode_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mode_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mode_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_movedim_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_movedim_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_msort_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_msort_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_msort_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_msort_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_msort_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_msort_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_msort_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mul_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mul_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mul_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_multinomial_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mv_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mv_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nan_to_num_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nan_to_num_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nan_to_num_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nan_to_num_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nan_to_num_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nanmedian_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nanmedian_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nanmedian_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nanmedian_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nanmedian_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nanmedian_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nansum_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_nansum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nansum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_narrow_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_narrow_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_narrow_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_narrow_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_narrow_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_narrow_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_native_dropout_backward_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_native_dropout_backward_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_ne_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ne_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_ne_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_ne_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_ne_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_neg_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_neg_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_neg_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_neg_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_neg_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_empty_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_empty_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_empty_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_empty_strided_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_empty_strided_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_empty_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_full_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_full_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_full_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_full_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_ones_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_ones_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_ones_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_ones_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_zeros_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_zeros_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_zeros_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_zeros_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_zeros_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_new_zeros_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nextafter_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_alpha_dropout_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_avg_pool1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_avg_pool2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_batch_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_bilinear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_bilinear_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_binary_cross_entropy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_binary_cross_entropy_with_logits_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_channel_shuffle_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_channel_shuffle_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_channel_shuffle_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_channel_shuffle_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv2d_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv2d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose1d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose2d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose2d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_cosine_embedding_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_cosine_embedding_loss_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_cosine_similarity_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_cross_entropy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_ctc_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_dropout2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_dropout_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_elu_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_embedding_bag_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_without_train_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_fractional_max_pool2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_fractional_max_pool3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_gelu_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_gelu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_glu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_grid_sample_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_group_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardsigmoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardsigmoid_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardsigmoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardtanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardtanh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardtanh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardtanh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hinge_embedding_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_area_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_bicubic_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_bicubic_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_bicubic_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_bicubic_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_bilinear_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_linear_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_nearest_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_nearest_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_trilinear_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_layer_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_linear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_local_response_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_local_response_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_logsigmoid_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_margin_ranking_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_margin_ranking_loss_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_margin_ranking_loss_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_margin_ranking_loss_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_pool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_pool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_pool3d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool1d_grad_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool1d_grad_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool2d_grad_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool2d_grad_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_mish_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_nll_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_constant_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_constant_cpu_float32, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_replicate_negative_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pairwise_distance_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pairwise_distance_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pairwise_distance_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pairwise_distance_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_shuffle_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_shuffle_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_shuffle_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_shuffle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_shuffle_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_shuffle_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_unshuffle_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_unshuffle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_unshuffle_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_poisson_nll_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_poisson_nll_loss_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_prelu_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_prelu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu6_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu6_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu6_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_selu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_selu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_silu_complex_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_silu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_smooth_l1_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_soft_margin_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_soft_margin_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softmin_with_dtype_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softmin_with_dtype_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softmin_with_dtype_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softmin_with_dtype_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softplus_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softsign_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softsign_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_tanhshrink_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_tanhshrink_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_tanhshrink_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_tanhshrink_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_threshold_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_threshold_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_with_distance_loss_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_unfold_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_upsample_bilinear_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_upsample_nearest_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_static_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_static_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_fro_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_fro_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_inf_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_nuc_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_normal_in_place_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_normal_number_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_ones_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_ones_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_ones_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ones_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_ones_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ones_like_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_ormqr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_outer_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_outer_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_pca_lowrank_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_polar_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_polar_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_0_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_0_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_1_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_1_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_2_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_3_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_4_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_4_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_polygamma_polygamma_n_4_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_positive_cpu_complex128, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_randn_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_randn_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_randn_like_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_randn_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_ravel_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_ravel_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_real_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_real_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_real_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_take_along_dim_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_take_along_dim_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_take_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_take_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_take_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_tan_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_tan_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_tan_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_tanh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_tanh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_tanh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_tanh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_tanh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_tensor_split_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_tensor_split_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_tensordot_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_tensordot_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_tensordot_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_tile_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_tile_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_tile_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_tile_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_to_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_to_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_to_sparse_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_to_sparse_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_to_sparse_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_torch_ops_aten__safe_softmax_default_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_torch_ops_aten__safe_softmax_default_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_trace_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_trace_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapezoid_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapz_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapz_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapz_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_triangular_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_triangular_solve_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_triangular_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_tril_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_tril_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_tril_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_tril_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_triu_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_triu_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_triu_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_triu_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_true_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_uint64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_int64, test/test_meta.py::TestMetaCPU::test_nan_to_num_cpu
2025-03-14T05:48:32.5500571Z 
2025-03-14T05:48:35.3881083Z Running torch_np/numpy_tests/core/test_getlimits 1/1 ... [2025-03-14 05:48:35.387643]
2025-03-14T05:48:35.3882061Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:48:35.3884043Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_getlimits.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:48:35.388009]
2025-03-14T05:48:39.2083303Z 
2025-03-14T05:48:39.2084411Z torch_np/numpy_tests/core/test_getlimits 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_getlimits_1.1_84f4ff3d5f49077c_.log
2025-03-14T05:48:39.2090773Z Running 17 items in this shard: test/torch_np/numpy_tests/core/test_getlimits.py::TestPythonFloat::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestHalf::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestSingle::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestDouble::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestFinfo::test_basic, test/torch_np/numpy_tests/core/test_getlimits.py::TestFinfo::test_basic_missing, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_basic, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T0, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T1, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T2, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T3, test/torch_np/numpy_tests/core/test_getlimits.py::TestRepr::test_finfo_repr, test/torch_np/numpy_tests/core/test_getlimits.py::TestRepr::test_iinfo_repr, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_instances, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_known_types, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_plausible_finfo, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_subnormal_warning
2025-03-14T05:48:39.2096553Z 
2025-03-14T05:48:42.7954167Z Running torch_np/numpy_tests/lib/test_shape_base_ 1/1 ... [2025-03-14 05:48:42.795046]
2025-03-14T05:48:42.7954899Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:48:42.7956465Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_shape_base_.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:48:42.795374]
2025-03-14T05:48:46.8150950Z 
2025-03-14T05:48:46.8152038Z torch_np/numpy_tests/lib/test_shape_base_ 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_shape_base__1.1_8d232d54ff21c45e_.log
2025-03-14T05:48:46.8177798Z Running 73 items in this shard: test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_argequivalent, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_broadcast, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_invalid, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestPutAlongAxis::test_broadcast, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestPutAlongAxis::test_replace_max, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_0d_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_3d, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_axis_insertion, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_axis_insertion_ma, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_scalar_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_simple101, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_tuple_func1d, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_with_iterable_object, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyOverAxes::test_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_axis_out_of_range, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_axis_tuple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_functionality, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_repeated_axis, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_high_bound, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_low_bound, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_0_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_cols, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_default, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_rows, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_rows_greater_max_int32, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSplit::test_equal_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSplit::test_unequal_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_1D_arrays, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_2D_arrays, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_generator, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_2D_array2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_generator, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_3D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_basic_2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_axis, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_axis_handling, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_contiguous, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_type, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a0_shape_b0, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a1_shape_b1, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a2_shape_b2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a3_shape_b3, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a4_shape_b4, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a5_shape_b5, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_kroncompare, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_tile_one_repetition_on_array_gh4679, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestMayShareMemory::test_basic
2025-03-14T05:48:46.8202747Z 
2025-03-14T05:48:50.4589429Z Running test_view_ops 1/1 ... [2025-03-14 05:48:50.458591]
2025-03-14T05:48:50.4589898Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:48:50.4592374Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_view_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:48:50.458978]
2025-03-14T05:49:07.1953389Z 
2025-03-14T05:49:07.1954634Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_a9b92586aea93321_.log
2025-03-14T05:49:07.2193030Z Running 442 items in this shard: test/test_view_ops.py::TestViewOpsCPU::test_T_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_assignment_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_gradients_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_ellipses_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_newaxis_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_slice_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_chunk_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_bfloat16, 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test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_view_lazy, test/test_view_ops.py::TestOldViewOpsCPU::test_T_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_as_strided_overflow_storage_offset_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_gradient_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_big_transpose_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_shapes_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_tensors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_chunk_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_conj_neg_view_numpy_error_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_contiguous_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_crow_col_indices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_empty_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_expand_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_flatten_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize__cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize_as_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_tensor_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_python_types_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_ravel_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_preserves_strides_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_overflow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_split_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_t_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_errors_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_unsqueeze_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_empty_cpu
2025-03-14T05:49:07.2428597Z 
2025-03-14T05:49:10.9357388Z Running test_jit_autocast 1/1 ... [2025-03-14 05:49:10.935361]
2025-03-14T05:49:10.9357879Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:49:10.9359483Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_autocast.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:49:10.935679]
2025-03-14T05:49:27.1760649Z 
2025-03-14T05:49:27.1761808Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_5b97818c1b926a60_.log
2025-03-14T05:49:27.1777092Z Running 54 items in this shard: test/test_jit_autocast.py::TestAutocast::test_autocast_api, test/test_jit_autocast.py::TestAutocast::test_autocast_api_not_supported, test/test_jit_autocast.py::TestAutocast::test_autocast_autodiff, test/test_jit_autocast.py::TestAutocast::test_autocast_decorator, test/test_jit_autocast.py::TestAutocast::test_autocast_decorator_outside_jit, test/test_jit_autocast.py::TestAutocast::test_autocast_mixed_dtypes, test/test_jit_autocast.py::TestAutocast::test_callees, test/test_jit_autocast.py::TestAutocast::test_callees_with_autocast_off, test/test_jit_autocast.py::TestAutocast::test_callees_with_autocast_on, test/test_jit_autocast.py::TestAutocast::test_conditional_autocast, test/test_jit_autocast.py::TestAutocast::test_control_flow, test/test_jit_autocast.py::TestAutocast::test_divergent_autocast, test/test_jit_autocast.py::TestAutocast::test_divergent_types, test/test_jit_autocast.py::TestAutocast::test_duplicate_inputs, test/test_jit_autocast.py::TestAutocast::test_eager_and_script, test/test_jit_autocast.py::TestAutocast::test_explicit_casts, test/test_jit_autocast.py::TestAutocast::test_fp32_policy, test/test_jit_autocast.py::TestAutocast::test_fp32_policy_with_fp64, test/test_jit_autocast.py::TestAutocast::test_fp32_set_opt_dtype_policy, test/test_jit_autocast.py::TestAutocast::test_fp32_set_opt_dtype_policy_fp64, test/test_jit_autocast.py::TestAutocast::test_ignore_amp, test/test_jit_autocast.py::TestAutocast::test_implicitly_nested_autocast, test/test_jit_autocast.py::TestAutocast::test_inplace, test/test_jit_autocast.py::TestAutocast::test_jit_autocast_softmax_cpu, test/test_jit_autocast.py::TestAutocast::test_jit_autocast_softmax_gpu, test/test_jit_autocast.py::TestAutocast::test_jit_call_method_under_autocast, test/test_jit_autocast.py::TestAutocast::test_jit_executor_under_autocast, test/test_jit_autocast.py::TestAutocast::test_jit_freeze_autocast_basic, test/test_jit_autocast.py::TestAutocast::test_jit_freeze_autocast_constants, test/test_jit_autocast.py::TestAutocast::test_jit_generic_autocast, test/test_jit_autocast.py::TestAutocast::test_linear_bf16, test/test_jit_autocast.py::TestAutocast::test_minimal, test/test_jit_autocast.py::TestAutocast::test_minimal_cpu, test/test_jit_autocast.py::TestAutocast::test_minimal_off, test/test_jit_autocast.py::TestAutocast::test_nested_autocast, test/test_jit_autocast.py::TestAutocast::test_promote_policy, test/test_jit_autocast.py::TestAutocast::test_promote_policy_fp64, test/test_jit_autocast.py::TestAutocast::test_reused_autocast, test/test_jit_autocast.py::TestAutocast::test_reused_autocast_expr, test/test_jit_autocast.py::TestAutocast::test_runtime_autocast_state, test/test_jit_autocast.py::TestAutocast::test_runtime_autocast_state_expr, test/test_jit_autocast.py::TestAutocast::test_script_and_tracing, test/test_jit_autocast.py::TestAutocast::test_script_and_tracing_with_autocast, test/test_jit_autocast.py::TestAutocast::test_script_module, test/test_jit_autocast.py::TestAutocast::test_tracing_and_script, test/test_jit_autocast.py::TestAutocast::test_tracing_with_autocast_and_script, test/test_jit_autocast.py::TestJitTraceAutocast::test_cat_promote, test/test_jit_autocast.py::TestJitTraceAutocast::test_generate_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_nchw_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_nhwc_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_cpu, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_cuda, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_enable_and_check, test/test_jit_autocast.py::TestJitTraceAutocast::test_scripted_aliasing
2025-03-14T05:49:27.1792206Z 
2025-03-14T05:49:31.0926009Z Running nn/test_init 1/1 ... [2025-03-14 05:49:31.092245]
2025-03-14T05:49:31.0926468Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:49:31.0928806Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_init.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:49:31.092626]
2025-03-14T05:49:38.7192219Z 
2025-03-14T05:49:38.7193424Z nn/test_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_init_1.1_ad84a48d4bfdf1c7_.log
2025-03-14T05:49:38.7210073Z Running 30 items in this shard: test/nn/test_init.py::TestNNInit::test_calculate_gain_leaky_relu, test/nn/test_init.py::TestNNInit::test_calculate_gain_leaky_relu_only_accepts_numbers, test/nn/test_init.py::TestNNInit::test_calculate_gain_linear, test/nn/test_init.py::TestNNInit::test_calculate_gain_nonlinear, test/nn/test_init.py::TestNNInit::test_calculate_gain_only_accepts_valid_nonlinearities, test/nn/test_init.py::TestNNInit::test_constant, test/nn/test_init.py::TestNNInit::test_deprecation, test/nn/test_init.py::TestNNInit::test_dirac_identity, test/nn/test_init.py::TestNNInit::test_dirac_only_works_on_3_4_5d_inputs, test/nn/test_init.py::TestNNInit::test_dirac_properties, test/nn/test_init.py::TestNNInit::test_eye, test/nn/test_init.py::TestNNInit::test_eye_only_works_on_2d_inputs, test/nn/test_init.py::TestNNInit::test_kaiming_normal, test/nn/test_init.py::TestNNInit::test_kaiming_normal_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_kaiming_normal_warning_on_0element_tensor, test/nn/test_init.py::TestNNInit::test_kaiming_uniform, test/nn/test_init.py::TestNNInit::test_kaiming_uniform_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_kaiming_uniform_warning_on_0element_tensor, test/nn/test_init.py::TestNNInit::test_normal, test/nn/test_init.py::TestNNInit::test_ones_and_zeros, test/nn/test_init.py::TestNNInit::test_orthogonal, test/nn/test_init.py::TestNNInit::test_sparse_default_std, test/nn/test_init.py::TestNNInit::test_sparse_only_works_on_2d_inputs, test/nn/test_init.py::TestNNInit::test_trunc_normal, test/nn/test_init.py::TestNNInit::test_trunc_normal_generator, test/nn/test_init.py::TestNNInit::test_uniform, test/nn/test_init.py::TestNNInit::test_xavier_normal, test/nn/test_init.py::TestNNInit::test_xavier_normal_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_xavier_uniform, test/nn/test_init.py::TestNNInit::test_xavier_uniform_errors_on_inputs_smaller_than_2d
2025-03-14T05:49:38.7224201Z 
2025-03-14T05:49:42.7488506Z Running test_xnnpack_integration 1/1 ... [2025-03-14 05:49:42.748387]
2025-03-14T05:49:42.7489367Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:49:42.7491400Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_xnnpack_integration.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:49:42.748739]
2025-03-14T05:49:57.9270739Z 
2025-03-14T05:49:57.9272363Z test_xnnpack_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_xnnpack_integration_1.1_f73c9d802a4669b3_.log
2025-03-14T05:49:57.9281037Z Running 12 items in this shard: test/test_xnnpack_integration.py::TestXNNPACKOps::test_conv2d, test/test_xnnpack_integration.py::TestXNNPACKOps::test_conv2d_transpose, test/test_xnnpack_integration.py::TestXNNPACKOps::test_linear, test/test_xnnpack_integration.py::TestXNNPACKOps::test_linear_1d_input, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_combined_model, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_conv2d, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_conv2d_transpose, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_linear, test/test_xnnpack_integration.py::TestXNNPACKRewritePass::test_decomposed_linear, test/test_xnnpack_integration.py::TestXNNPACKRewritePass::test_linear, test/test_xnnpack_integration.py::TestXNNPACKConv1dTransformPass::test_conv1d_basic, test/test_xnnpack_integration.py::TestXNNPACKConv1dTransformPass::test_conv1d_with_relu_fc
2025-03-14T05:49:57.9288722Z 
2025-03-14T05:50:02.1950256Z Running profiler/test_profiler_tree 1/1 ... [2025-03-14 05:50:02.194587]
2025-03-14T05:50:02.1951198Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:50:02.1953386Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_profiler_tree.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:50:02.194963]
2025-03-14T05:50:05.8149940Z 
2025-03-14T05:50:05.8151076Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_b4c8e86d630d2c3e_.log
2025-03-14T05:50:05.8156504Z Running 10 items in this shard: test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_detailed, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_with_stream, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory_and_stack, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_record_function, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_modules, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_dispatch, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_function
2025-03-14T05:50:05.8161401Z 
2025-03-14T05:50:09.5387236Z Running torch_np/test_ndarray_methods 1/1 ... [2025-03-14 05:50:09.538253]
2025-03-14T05:50:09.5388198Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:50:09.5390341Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_ndarray_methods.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:50:09.538631]
2025-03-14T05:50:17.3656886Z 
2025-03-14T05:50:17.3658423Z torch_np/test_ndarray_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ndarray_methods_1.1_7af34dae1b2bf118_.log
2025-03-14T05:50:17.4037910Z Running 342 items in this shard: test/torch_np/test_ndarray_methods.py::TestIndexing::test_indexing_simple, test/torch_np/test_ndarray_methods.py::TestIndexing::test_setitem, test/torch_np/test_ndarray_methods.py::TestReshape::test_reshape_function, test/torch_np/test_ndarray_methods.py::TestReshape::test_reshape_method, test/torch_np/test_ndarray_methods.py::TestTranspose::test_transpose_function, test/torch_np/test_ndarray_methods.py::TestTranspose::test_transpose_method, test/torch_np/test_ndarray_methods.py::TestRavel::test_ravel_function, test/torch_np/test_ndarray_methods.py::TestRavel::test_ravel_method, test/torch_np/test_ndarray_methods.py::TestNonzero::test_array_method, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_onedim, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_trivial, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_twodim, test/torch_np/test_ndarray_methods.py::TestNonzero::test_sparse, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_all_method_max, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_all_method_min, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size0_axis0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size0_axis0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size10_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size10_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size11_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size11_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size12_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size12_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size13_axis13_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size13_axis13_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size14_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size14_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size15_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size15_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size16_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size16_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size17_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size17_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size18_axis18_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size18_axis18_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size19_axis_-3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size19_axis_-3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size1_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size1_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size20_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size20_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size21_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size21_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size22_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size22_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size23_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size23_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size24_axis_2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size24_axis_2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size25_axis25_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size25_axis25_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size26_axis_-3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size26_axis_-3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size27_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size27_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size28_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size28_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size29_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size29_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size2_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size2_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size30_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size30_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size31_axis_2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size31_axis_2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size32_axis32_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size32_axis32_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size33_axis_-4_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size33_axis_-4_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size34_axis_-3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size34_axis_-3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size35_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size35_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size36_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size36_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size37_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size37_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size38_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size38_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size39_axis_2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size39_axis_2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size3_axis3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size3_axis3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size40_axis_3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size40_axis_3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size41_axis41_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size41_axis41_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size42_axis_-4_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size42_axis_-4_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size43_axis_-3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size43_axis_-3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size44_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size44_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size45_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size45_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size46_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size46_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size47_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size47_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size48_axis_2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size48_axis_2_method1, 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test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_plain, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_rvar, test/torch_np/test_ndarray_methods.py::TestIter::test_iter_1d, test/torch_np/test_ndarray_methods.py::TestIter::test_iter_2d
2025-03-14T05:50:17.4316398Z 
2025-03-14T05:50:21.6613183Z Running test_jit 1/1 ... [2025-03-14 05:50:21.660825]
2025-03-14T05:50:21.6613919Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:50:21.6620076Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:50:21.661603]
2025-03-14T05:52:06.7014515Z 
2025-03-14T05:52:06.7015714Z test_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_1.1_c5cdc143f2d27ea9_.log
2025-03-14T05:52:06.8245454Z Running 2456 items in this shard: test/test_jit.py::TestTracer::test_call_traced_fn_from_traced_module, test/test_jit.py::TestTracer::test_call_traced_module_from_traced_module, test/test_jit.py::TestTracer::test_canonicalize_tensor_iterator, test/test_jit.py::TestTracer::test_constant, test/test_jit.py::TestTracer::test_conv, test/test_jit.py::TestTracer::test_export_no_reorder, test/test_jit.py::TestTracer::test_force_outplace_check_fill, test/test_jit.py::TestTracer::test_force_outplace_check_zero, test/test_jit.py::TestTracer::test_ge, test/test_jit.py::TestTracer::test_ge_cuda, test/test_jit.py::TestTracer::test_ge_optimized, test/test_jit.py::TestTracer::test_ge_unoptimized, test/test_jit.py::TestTracer::test_index_put, test/test_jit.py::TestTracer::test_index_put_trace_with_view, test/test_jit.py::TestTracer::test_index_put_trace_without_view, test/test_jit.py::TestTracer::test_inplace_check, test/test_jit.py::TestTracer::test_inplace_copy, test/test_jit.py::TestTracer::test_inplace_copy_force_outplace, test/test_jit.py::TestTracer::test_inplace_flags, test/test_jit.py::TestTracer::test_inplace_transplant, test/test_jit.py::TestTracer::test_inplace_warn, test/test_jit.py::TestTracer::test_input_dict_checkTrace_mut, test/test_jit.py::TestTracer::test_input_dict_empty, test/test_jit.py::TestTracer::test_input_dict_empty_list, test/test_jit.py::TestTracer::test_input_dict_insertion_order, test/test_jit.py::TestTracer::test_input_dict_of_dicts, test/test_jit.py::TestTracer::test_input_dict_of_lists, test/test_jit.py::TestTracer::test_input_dict_recursive, test/test_jit.py::TestTracer::test_input_dict_remembers_keys, test/test_jit.py::TestTracer::test_input_dict_unify, test/test_jit.py::TestTracer::test_input_flatten, test/test_jit.py::TestTracer::test_input_list_mixed_type, test/test_jit.py::TestTracer::test_input_list_of_tuples, test/test_jit.py::TestTracer::test_input_list_toplevel_flatten, 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test/test_jit.py::TestList::test_mutable_list_insert_out_of_bounds, test/test_jit.py::TestList::test_mutable_list_nested_loop, test/test_jit.py::TestList::test_mutable_list_pop, test/test_jit.py::TestList::test_mutable_list_pop2, test/test_jit.py::TestList::test_mutable_list_pop_at, test/test_jit.py::TestList::test_mutable_list_pop_at2, test/test_jit.py::TestList::test_mutable_list_pop_at_negative, test/test_jit.py::TestList::test_mutable_list_pop_at_negative2, test/test_jit.py::TestList::test_mutable_list_pop_empty, test/test_jit.py::TestList::test_mutable_list_pop_slice, test/test_jit.py::TestList::test_mutable_list_remove, test/test_jit.py::TestList::test_mutable_list_remove2, test/test_jit.py::TestList::test_mutable_list_remove_not_existing, test/test_jit.py::TestList::test_mutable_list_remove_tensor, test/test_jit.py::TestList::test_mutable_list_reverse, test/test_jit.py::TestList::test_mutable_list_reverse_empty, test/test_jit.py::TestList::test_mutable_tensor_list_reverse, test/test_jit.py::TestList::test_no_element_type_annotation, test/test_jit.py::TestList::test_slice_index, test/test_jit.py::TestList::test_tensor_list_count, test/test_jit.py::TestList::test_tensor_list_count_not_existing, test/test_jit.py::TestList::test_tensor_list_index, test/test_jit.py::TestList::test_tensor_list_index_not_existing, test/test_jit.py::TestList::test_to_list, test/test_jit.py::TestList::test_to_list_gpu, test/test_jit.py::TestDict::test_aug_assign, test/test_jit.py::TestDict::test_basic, test/test_jit.py::TestDict::test_clear, test/test_jit.py::TestDict::test_copy, test/test_jit.py::TestDict::test_del, test/test_jit.py::TestDict::test_dict_bool_conversion, test/test_jit.py::TestDict::test_dict_preserves_order, test/test_jit.py::TestDict::test_dict_to_python, test/test_jit.py::TestDict::test_dict_variance, test/test_jit.py::TestDict::test_get, test/test_jit.py::TestDict::test_get_boolkey, test/test_jit.py::TestDict::test_items, test/test_jit.py::TestDict::test_key_type, test/test_jit.py::TestDict::test_keys, test/test_jit.py::TestDict::test_len, test/test_jit.py::TestDict::test_loop, test/test_jit.py::TestDict::test_membership, test/test_jit.py::TestDict::test_mutability, test/test_jit.py::TestDict::test_optional_dict_construct, test/test_jit.py::TestDict::test_ordered_dict, test/test_jit.py::TestDict::test_pop, test/test_jit.py::TestDict::test_popitem, test/test_jit.py::TestDict::test_setdefault, test/test_jit.py::TestDict::test_type_annotation_missing_contained_type, test/test_jit.py::TestDict::test_update, test/test_jit.py::TestDict::test_update_existing_key, test/test_jit.py::TestDict::test_values, test/test_jit.py::TestDict::test_view, test/test_jit.py::TestNamedTuple::test_namedtuple, test/test_jit.py::TestNamedTuple::test_namedtuple_as_attr, test/test_jit.py::TestNamedTuple::test_namedtuple_constant, test/test_jit.py::TestNamedTuple::test_namedtuple_input_forwardref, test/test_jit.py::TestNamedTuple::test_namedtuple_inside_forwardref, test/test_jit.py::TestNamedTuple::test_namedtuple_kwarg_construct, test/test_jit.py::TestNamedTuple::test_namedtuple_lower, test/test_jit.py::TestNamedTuple::test_namedtuple_resolution, test/test_jit.py::TestNamedTuple::test_namedtuple_resolution_forwardref, test/test_jit.py::TestNamedTuple::test_namedtuple_serialization, test/test_jit.py::TestNamedTuple::test_namedtuple_slice_unpack, test/test_jit.py::TestNamedTuple::test_namedtuple_type_annotation, test/test_jit.py::TestNamedTuple::test_namedtuple_wrong_types, test/test_jit.py::TestNamedTuple::test_return_named_tuple, test/test_jit.py::TestScriptDict::test_bool, test/test_jit.py::TestScriptDict::test_contains, test/test_jit.py::TestScriptDict::test_delitem, test/test_jit.py::TestScriptDict::test_getitem, test/test_jit.py::TestScriptDict::test_items, test/test_jit.py::TestScriptDict::test_iter, test/test_jit.py::TestScriptDict::test_len, test/test_jit.py::TestScriptDict::test_nested, test/test_jit.py::TestScriptDict::test_reference_semantics, test/test_jit.py::TestScriptDict::test_repr, test/test_jit.py::TestScriptDict::test_setitem, test/test_jit.py::TestScriptList::test_append, test/test_jit.py::TestScriptList::test_bool, test/test_jit.py::TestScriptList::test_clear, test/test_jit.py::TestScriptList::test_contains, test/test_jit.py::TestScriptList::test_count, test/test_jit.py::TestScriptList::test_defaultdict, test/test_jit.py::TestScriptList::test_delitem, test/test_jit.py::TestScriptList::test_extend, test/test_jit.py::TestScriptList::test_getitem, test/test_jit.py::TestScriptList::test_insert, test/test_jit.py::TestScriptList::test_iter, test/test_jit.py::TestScriptList::test_len, test/test_jit.py::TestScriptList::test_nested, test/test_jit.py::TestScriptList::test_pop, test/test_jit.py::TestScriptList::test_reference_semantics, test/test_jit.py::TestScriptList::test_remove, test/test_jit.py::TestScriptList::test_repr, test/test_jit.py::TestScriptList::test_setitem, test/test_jit.py::TestAsync::test_async_future_type_python, test/test_jit.py::TestAsync::test_async_grad_guard_no_grad, test/test_jit.py::TestAsync::test_async_grad_guard_with_grad, test/test_jit.py::TestAsync::test_async_kwargs, test/test_jit.py::TestAsync::test_async_parsing, test/test_jit.py::TestAsync::test_async_python, test/test_jit.py::TestAsync::test_async_script, test/test_jit.py::TestAsync::test_async_script_capture, test/test_jit.py::TestAsync::test_async_script_error, test/test_jit.py::TestAsync::test_async_script_multi_forks, test/test_jit.py::TestAsync::test_async_script_multi_waits, test/test_jit.py::TestAsync::test_async_script_nested, test/test_jit.py::TestAsync::test_async_script_no_script_mod, test/test_jit.py::TestAsync::test_async_script_trace, test/test_jit.py::TestAsync::test_future_subtyping, test/test_jit.py::TestAsync::test_no_future_subtype_message, test/test_jit.py::TestAsync::test_trace_fork_wait, test/test_jit.py::TestAsync::test_trace_fork_wait_inline, test/test_jit.py::TestAsync::test_trace_fork_wait_leaking, test/test_jit.py::TestAsync::test_trace_fork_wait_list_modulecalls, test/test_jit.py::TestAsync::test_trace_modulecalls_with_different_output_types, test/test_jit.py::TestAwait::test_await_class_arg, test/test_jit.py::TestAwait::test_await_class_return, test/test_jit.py::TestAwait::test_await_eager_lazy, test/test_jit.py::TestAwait::test_await_func_arg, test/test_jit.py::TestAwait::test_await_getattr_implicit_convertion, test/test_jit.py::TestAwait::test_await_isinstance, test/test_jit.py::TestAwait::test_await_multiout_save, test/test_jit.py::TestAwait::test_await_nested, test/test_jit.py::TestAwait::test_await_out_of_interpreter, test/test_jit.py::TestAwait::test_await_python, test/test_jit.py::TestAwait::test_await_type_python, test/test_jit.py::TestAwait::test_awaitable_to_await, test/test_jit.py::TestAwait::test_eager_await_non_scriptable, test/test_jit.py::TestAwait::test_jit_trace, test/test_jit.py::TestAwait::test_nowait, test/test_jit.py::TestAwait::test_nowait_class, test/test_jit.py::TestAwait::test_script, test/test_jit.py::TestDataParallel::test_python_submodule_script, test/test_jit.py::TestDataParallel::test_shared_module, test/test_jit.py::TestDataParallel::test_tensor_sharing, test/test_jit.py::TestDataParallel::test_tensor_sharing_with_forward, test/test_jit.py::TestDataParallel::test_traced_module, test/test_jit.py::TestModels::test_alexnet, test/test_jit.py::TestModels::test_dcgan_models, test/test_jit.py::TestModels::test_dcgan_models_cuda, test/test_jit.py::TestModels::test_mnist, test/test_jit.py::TestModels::test_mnist_cuda, test/test_jit.py::TestModels::test_mnist_training_leaks_no_memory_cuda, test/test_jit.py::TestModels::test_neural_style, test/test_jit.py::TestModels::test_neural_style_cuda, test/test_jit.py::TestModels::test_reinforcement_learning, test/test_jit.py::TestModels::test_reinforcement_learning_cuda, test/test_jit.py::TestModels::test_script_module_script_resnet, test/test_jit.py::TestModels::test_script_module_trace_resnet18, test/test_jit.py::TestModels::test_snli, test/test_jit.py::TestModels::test_snli_cuda, test/test_jit.py::TestModels::test_super_resolution, test/test_jit.py::TestModels::test_super_resolution_cuda, test/test_jit.py::TestModels::test_time_sequence_prediction, test/test_jit.py::TestModels::test_vae, test/test_jit.py::TestModels::test_vae_cuda, test/test_jit.py::TestModules::test_script_module_with_constants_list, test/test_jit.py::TestAutodiffJit::test_autodiff_requires_grad_nograd, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs_profiled_twice, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs_side_effects, test/test_jit.py::TestAutodiffJit::test_undefined_tensor_lists, test/test_jit.py::TestAutodiffSubgraphSlicing::test_aliased_outputs, test/test_jit.py::TestAutodiffSubgraphSlicing::test_bias_as_arg, test/test_jit.py::TestAutodiffSubgraphSlicing::test_bias_as_module_attr, test/test_jit.py::TestAutodiffSubgraphSlicing::test_chunk_constant_script_ad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_constructed_bias, test/test_jit.py::TestAutodiffSubgraphSlicing::test_diff_graph_inline_threshold, test/test_jit.py::TestAutodiffSubgraphSlicing::test_differentiable_graph_ops_requires_grad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_does_not_create_cycles, test/test_jit.py::TestAutodiffSubgraphSlicing::test_does_not_merge_unrelated, test/test_jit.py::TestAutodiffSubgraphSlicing::test_has_profiled_info_aliasing_outputs, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merge_respects_aliasing, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_dense, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_down, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_up, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_without_cycles, test/test_jit.py::TestAutodiffSubgraphSlicing::test_prune_grad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_requires_grad_for_tensor_list, test/test_jit.py::TestAutodiffSubgraphSlicing::test_respects_lexical_scoping, test/test_jit.py::TestAutodiffSubgraphSlicing::test_simple_merge, test/test_jit.py::TestAutodiffSubgraphSlicing::test_simple_no_merge, test/test_jit.py::TestCustomOperators::test_calling_scripted_custom_op, test/test_jit.py::TestCustomOperators::test_calling_traced_custom_op, test/test_jit.py::TestCustomOperators::test_default_arguments_are_used, test/test_jit.py::TestCustomOperators::test_dynamic_op_registry, test/test_jit.py::TestCustomOperators::test_generic_list, test/test_jit.py::TestCustomOperators::test_getting_invalid_attr, test/test_jit.py::TestCustomOperators::test_passing_and_returning_lists, test/test_jit.py::TestCustomOperators::test_passing_one_positional_but_not_the_second, test/test_jit.py::TestCustomOperators::test_passing_too_few_args, test/test_jit.py::TestCustomOperators::test_passing_too_many_args, test/test_jit.py::TestCustomOperators::test_passing_unknown_kwargs, test/test_jit.py::TestCustomOperators::test_script_graph_contains_custom_op, test/test_jit.py::TestCustomOperators::test_script_graph_for_custom_ops_matches_traced_graph, test/test_jit.py::TestCustomOperators::test_simply_calling_an_operator, test/test_jit.py::TestCustomOperators::test_where_no_scalar, test/test_jit.py::TestGraphRewritePasses::test_fuse_linear, test/test_jit.py::TestClassType::test_cast_overloads, test/test_jit.py::TestClassType::test_class_attribute_wrong_type, test/test_jit.py::TestClassType::test_class_constant, test/test_jit.py::TestClassType::test_class_constructs_itself, test/test_jit.py::TestClassType::test_class_inheritance, test/test_jit.py::TestClassType::test_class_inheritance_implicit, test/test_jit.py::TestClassType::test_class_sorting, test/test_jit.py::TestClassType::test_class_specialization, test/test_jit.py::TestClassType::test_class_type_as_param, test/test_jit.py::TestClassType::test_classmethod, test/test_jit.py::TestClassType::test_conditional_set_attr, test/test_jit.py::TestClassType::test_custom_delete, test/test_jit.py::TestClassType::test_default_args, test/test_jit.py::TestClassType::test_get_attr, test/test_jit.py::TestClassType::test_get_attr_not_initialized, test/test_jit.py::TestClassType::test_get_with_method, test/test_jit.py::TestClassType::test_imported_classes, test/test_jit.py::TestClassType::test_in, test/test_jit.py::TestClassType::test_init_compiled_first, test/test_jit.py::TestClassType::test_interface, test/test_jit.py::TestClassType::test_optional_type_promotion, test/test_jit.py::TestClassType::test_out_of_order_methods, test/test_jit.py::TestClassType::test_overloaded_fn, test/test_jit.py::TestClassType::test_properties, test/test_jit.py::TestClassType::test_py_class_to_ivalue_missing_attribute, test/test_jit.py::TestClassType::test_python_interop, test/test_jit.py::TestClassType::test_recursive_class, test/test_jit.py::TestClassType::test_recursive_script_builtin_type_resolution, test/test_jit.py::TestClassType::test_recursive_script_module_builtin_type_resolution, test/test_jit.py::TestClassType::test_recursive_scripting, test/test_jit.py::TestClassType::test_recursive_scripting_failed, test/test_jit.py::TestClassType::test_reference_semantics, test/test_jit.py::TestClassType::test_save_load_with_classes, test/test_jit.py::TestClassType::test_save_load_with_classes_nested, test/test_jit.py::TestClassType::test_save_load_with_classes_returned, test/test_jit.py::TestClassType::test_schema_human_readable, test/test_jit.py::TestClassType::test_self_referential_method, test/test_jit.py::TestClassType::test_set_attr_in_method, test/test_jit.py::TestClassType::test_set_attr_non_initialized, test/test_jit.py::TestClassType::test_set_attr_type_mismatch, test/test_jit.py::TestClassType::test_staticmethod, test/test_jit.py::TestClassType::test_type_annotation, test/test_jit.py::TestClassType::test_type_annotations, test/test_jit.py::TestClassType::test_unresolved_class_attributes, test/test_jit.py::TestClassType::test_unused_method, test/test_jit.py::TestBuiltins::test_del, test/test_jit.py::TestBuiltins::test_del_multiple_operands, test/test_jit.py::TestBuiltins::test_has_attr, test/test_jit.py::TestBuiltins::test_has_attr_invalid_args, test/test_jit.py::TestTensorBuiltins::test_method_on_number, test/test_jit.py::TestTensorBuiltins::test_scalar_to_num_conversions, test/test_jit.py::TestTensorBuiltins::test_tensor_item, test/test_jit.py::TestTensorBuiltins::test_tensor_properties, test/test_jit.py::TestTensorBuiltins::test_tensor_subscript_assign, test/test_jit.py::TestTensorBuiltins::test_tensor_subscript_assign_device, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_inp_out, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_just_inp, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_just_out, test/test_jit.py::TestSymbolicShapeAnalysis::test_adaptive_avg_pool2d, test/test_jit.py::TestSymbolicShapeAnalysis::test_arange_shape, test/test_jit.py::TestSymbolicShapeAnalysis::test_binary_shape_fns_inplace, test/test_jit.py::TestSymbolicShapeAnalysis::test_binary_shape_functions, test/test_jit.py::TestSymbolicShapeAnalysis::test_conv_deconv, test/test_jit.py::TestSymbolicShapeAnalysis::test_convolution_backward, test/test_jit.py::TestSymbolicShapeAnalysis::test_cross_entropy_loss, test/test_jit.py::TestSymbolicShapeAnalysis::test_if_propagation, test/test_jit.py::TestSymbolicShapeAnalysis::test_partial_eval_graph_conv, test/test_jit.py::TestSymbolicShapeAnalysis::test_partial_eval_stitching, test/test_jit.py::TestSymbolicShapeAnalysis::test_refinement_through_graph_stitching, test/test_jit.py::TestSymbolicShapeAnalysis::test_register_function_error_checking, test/test_jit.py::TestSymbolicShapeAnalysis::test_returning_input_symbolic_shapes, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_analysis, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_concat, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_embedding_bag, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_function_includes, test/test_jit.py::TestSymbolicShapeAnalysis::test_shared_shape_graph, test/test_jit.py::TestSymbolicShapeAnalysis::test_size_and_sizes, test/test_jit.py::TestSymbolicShapeAnalysis::test_squeeze_dims, test/test_jit.py::TestSymbolicShapeAnalysis::test_stitching_concat, test/test_jit.py::TestSymbolicShapeAnalysis::test_stitching_multi_output, test/test_jit.py::TestSymbolicShapeAnalysis::test_sym_ir_parsing, test/test_jit.py::TestSymbolicShapeAnalysis::test_unary_shape_fns_inplace, test/test_jit.py::TestSymbolicShapeAnalysis::test_unary_shape_functions, test/test_jit.py::TestSymbolicShapeAnalysis::test_write, test/test_jit.py::TestOpDecompositions::test_op_decomposition, test/test_jit.py::TestOpDecompositions::test_registered_decomposition, test/test_jit.py::TestUnsupportedOps::test_factory_ops_requires_grad_fail, test/test_jit.py::TestUnsupportedOps::test_init_ops, test/test_jit.py::TestFreezing::test_freeze_interface_swapping_two_methods, test/test_jit.py::TestFreezing::test_freeze_interface_within_object, test/test_jit.py::TestFreezing::test_freeze_module, test/test_jit.py::TestFreezing::test_freeze_module_detach_gradient, test/test_jit.py::TestFreezing::test_freeze_module_in_training_mode, test/test_jit.py::TestFreezing::test_freeze_module_inlining, test/test_jit.py::TestFreezing::test_freeze_module_no_forward, test/test_jit.py::TestFreezing::test_freeze_module_return_self, test/test_jit.py::TestFreezing::test_freeze_module_return_sub_module, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr2, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr3, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr2, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr3, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr4, test/test_jit.py::TestFreezing::test_freeze_module_with_call_method, test/test_jit.py::TestFreezing::test_freeze_module_with_fork, test/test_jit.py::TestFreezing::test_freeze_module_with_fork2, test/test_jit.py::TestFreezing::test_freeze_module_with_fork_calling_module_method, test/test_jit.py::TestFreezing::test_freeze_module_with_helperfunction, test/test_jit.py::TestFreezing::test_freeze_module_with_inplace_mutable, test/test_jit.py::TestFreezing::test_freeze_module_with_list, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_dict, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_list, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_tensor, test/test_jit.py::TestFreezing::test_freeze_module_with_nested_fork, test/test_jit.py::TestFreezing::test_freeze_module_with_nestedaliasing, test/test_jit.py::TestFreezing::test_freeze_module_with_nestedaliasingscalar, test/test_jit.py::TestFreezing::test_freeze_module_with_non_static_module_container_index, test/test_jit.py::TestFreezing::test_freeze_module_with_overlapping_attrs, test/test_jit.py::TestFreezing::test_freeze_module_with_preserve_sub_module, test/test_jit.py::TestFreezing::test_freeze_module_with_preserve_sub_module_and_mutation, test/test_jit.py::TestFreezing::test_freeze_module_with_sharedclasstype, test/test_jit.py::TestFreezing::test_freeze_module_with_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_tensor, test/test_jit.py::TestFreezing::test_freeze_module_with_tuple, test/test_jit.py::TestFreezing::test_freeze_module_with_tupleoutput_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_attr, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_attribute_on_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_attribute_on_unused_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method2, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method_on_submodule, test/test_jit.py::TestFreezing::test_freeze_no_forward, test/test_jit.py::TestFreezing::test_freeze_non_interface_module_swap, test/test_jit.py::TestFreezing::test_freeze_non_module_class_getattr, test/test_jit.py::TestFreezing::test_freeze_recursive_interfaces, test/test_jit.py::TestFreezing::test_freeze_recursive_interfaces_same_name, test/test_jit.py::TestFreezing::test_freeze_recursive_interfaces_with_reassignment, test/test_jit.py::TestFreezing::test_freeze_with_interface_mutable, test/test_jit.py::TestFreezing::test_freeze_with_swapping_interfaces, test/test_jit.py::TestFreezing::test_module_getattr_indirection, test/test_jit.py::TestFreezing::test_module_with_shared_type_instances, test/test_jit.py::TestFrozenOptimizations::test_bn_not_broadcast_with_linear, test/test_jit.py::TestFrozenOptimizations::test_collapse_adjacent_conversions, test/test_jit.py::TestFrozenOptimizations::test_conv_add_folding, test/test_jit.py::TestFrozenOptimizations::test_conv_bn_folding, test/test_jit.py::TestFrozenOptimizations::test_conv_bn_folding_autocast_scenario_cuda, test/test_jit.py::TestFrozenOptimizations::test_conv_bn_folding_not_forward, test/test_jit.py::TestFrozenOptimizations::test_conv_hardswish, test/test_jit.py::TestFrozenOptimizations::test_conv_mul_add_bn, test/test_jit.py::TestFrozenOptimizations::test_conv_to_mkldnn, test/test_jit.py::TestFrozenOptimizations::test_conv_to_mkldnn_no_mkldnn, test/test_jit.py::TestFrozenOptimizations::test_freeze_conv_relu_fusion, test/test_jit.py::TestFrozenOptimizations::test_freeze_conv_relu_fusion_not_forward, test/test_jit.py::TestFrozenOptimizations::test_freeze_mkdlnn, test/test_jit.py::TestFrozenOptimizations::test_freeze_remove_dropout, test/test_jit.py::TestFrozenOptimizations::test_freeze_remove_feature_dropout, test/test_jit.py::TestFrozenOptimizations::test_hardswish_hardsigmoid, test/test_jit.py::TestFrozenOptimizations::test_incompatible_perf_formats, test/test_jit.py::TestFrozenOptimizations::test_linear_bn_folding, test/test_jit.py::TestFrozenOptimizations::test_linear_bn_folding_autocast_scenario_cuda, test/test_jit.py::TestFrozenOptimizations::test_linear_concat, test/test_jit.py::TestFrozenOptimizations::test_linear_concat_complex, test/test_jit.py::TestFrozenOptimizations::test_linear_concat_different_input, test/test_jit.py::TestFrozenOptimizations::test_linear_multiple_blocks, test/test_jit.py::TestFrozenOptimizations::test_linear_non_constant_weight, test/test_jit.py::TestFrozenOptimizations::test_linear_transpose, test/test_jit.py::TestFrozenOptimizations::test_maxpool_mkldnn, test/test_jit.py::TestFrozenOptimizations::test_mkldnn_fuser_broadcasting, test/test_jit.py::TestFrozenOptimizations::test_mkldnn_inplace_removal, test/test_jit.py::TestFrozenOptimizations::test_numel_less_than_size_with_padding, test/test_jit.py::TestFrozenOptimizations::test_optimize_freeze_module, test/test_jit.py::TestFrozenOptimizations::test_pool2d_batchnorm, test/test_jit.py::TestFrozenOptimizations::test_pool3d_batchnorm, test/test_jit.py::TestFrozenOptimizations::test_remove_detach, test/test_jit.py::TestFrozenOptimizations::test_remove_detach_not_applied, test/test_jit.py::TestFrozenOptimizations::test_scalar_mul, test/test_jit.py::TestMKLDNNReinplacing::test_always_alive_values, test/test_jit.py::TestMKLDNNReinplacing::test_merge_liveness, test/test_jit.py::TestMKLDNNReinplacing::test_successful, test/test_jit.py::TestMKLDNNReinplacing::test_switch_inputs_to_inplace, test/test_jit.py::TestPeephole::test_conv_dim_folding, test/test_jit.py::TestPeephole::test_integer_refinement, test/test_jit.py::TestPeephole::test_noop_peephole, test/test_jit.py::TestPeephole::test_normalized_is_op, test/test_jit.py::TestPeephole::test_normalized_isnot_op, test/test_jit.py::TestPeephole::test_normalized_rsub, test/test_jit.py::TestPeephole::test_optimize_out_comparison_same_value, test/test_jit.py::TestPeephole::test_peephole, test/test_jit.py::TestPeephole::test_peephole_add_zero, test/test_jit.py::TestPeephole::test_peephole_arith, test/test_jit.py::TestPeephole::test_peephole_cuda, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_dict_modified, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_get_input_arg, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_keys_might_overlap, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_missing_key, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_overlapping_keys, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_unsupported_type, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_simple, test/test_jit.py::TestPeephole::test_peephole_dict_len, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_keys_might_overlap, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_overlapping_keys, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_unsupported_type, test/test_jit.py::TestPeephole::test_peephole_dynamic, test/test_jit.py::TestPeephole::test_peephole_int, test/test_jit.py::TestPeephole::test_peephole_len_list, test/test_jit.py::TestPeephole::test_peephole_list_len, test/test_jit.py::TestPeephole::test_peephole_list_ops, test/test_jit.py::TestPeephole::test_peephole_no_output_aliasing, test/test_jit.py::TestPeephole::test_peephole_optional_refine, test/test_jit.py::TestPeephole::test_peephole_slice_all_three_args, test/test_jit.py::TestPeephole::test_peephole_slice_one_empty_arg, test/test_jit.py::TestPeephole::test_peephole_slice_optimization_not_applied_list_modified, test/test_jit.py::TestPeephole::test_peephole_slice_optimization_not_applied_non_const_args, test/test_jit.py::TestPeephole::test_peephole_slice_two_empty_args, test/test_jit.py::TestPeephole::test_peephole_type_refinements, test/test_jit.py::TestPeephole::test_peephole_with_non_output_writes, test/test_jit.py::TestPeephole::test_peephole_with_writes, test/test_jit.py::TestPeephole::test_refine_integer_values, test/test_jit.py::TestPeephole::test_short_circuit_optimization, test/test_jit.py::TestAliasAnalysis::test_becomes_wildcard_annotations, test/test_jit.py::TestAliasAnalysis::test_multiple_compilation_units, test/test_jit.py::TestAliasAnalysis::test_nested_list_construct_not_wildcard, test/test_jit.py::TestAliasAnalysis::test_recursive_calls, test/test_jit.py::TestSaveLoad::test_different_functions, test/test_jit.py::TestSaveLoad::test_different_interfaces, test/test_jit.py::TestSaveLoad::test_different_modules, test/test_jit.py::TestSaveLoad::test_many_collisions, test/test_jit.py::TestSaveLoad::test_save_load_large_string_attribute, test/test_jit.py::TestSaveLoad::test_save_load_meta_tensors, test/test_jit.py::TestSaveLoad::test_save_load_meta_tensors_to_device, test/test_jit.py::TestSaveLoad::test_save_load_params_buffers_submodules, test/test_jit.py::TestSaveLoad::test_save_load_using_pathlib, test/test_jit.py::TestSaveLoad::test_save_load_with_extra_files, test/test_jit.py::TestSaveLoad::test_save_load_with_saved_traced_inputs, test/test_jit.py::TestSaveLoad::test_save_namedtuple_input_only, test/test_jit.py::TestSaveLoad::test_save_namedtuple_input_only_forwardref, test/test_jit.py::TestSaveLoad::test_save_namedtuple_output_only, test/test_jit.py::TestSaveLoad::test_save_nonexit_file, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_functions, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_interfaces, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_modules, test/test_jit.py::TestSaveLoadFlatbuffer::test_many_collisions, test/test_jit.py::TestSaveLoadFlatbuffer::test_module_info_flatbuffer, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_load_params_buffers_submodules, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_load_using_pathlib, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_load_with_extra_files, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_namedtuple_input_only, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_namedtuple_output_only, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_inplace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_reciprocal, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_scalar, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor_inplace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor_out, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_linspace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_linspace_out, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_logspace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_logspace_out, test/test_jit.py::TestModuleContainers::test_custom_container_forward, test/test_jit.py::TestModuleContainers::test_empty_dict_override_contains, test/test_jit.py::TestModuleContainers::test_module_inplace_construct, test/test_jit.py::TestModuleContainers::test_module_interface_special_methods, test/test_jit.py::TestModuleContainers::test_module_properties, test/test_jit.py::TestModuleContainers::test_moduledict, test/test_jit.py::TestModuleContainers::test_moduledict_getitem, test/test_jit.py::TestModuleContainers::test_moduledict_keyerror, test/test_jit.py::TestModuleContainers::test_normal_list_attribute_with_modules_error, test/test_jit.py::TestModuleContainers::test_parameterdict_script_getitem, test/test_jit.py::TestModuleContainers::test_parameterlist_script_getitem, test/test_jit.py::TestModuleContainers::test_parameterlist_script_iter, test/test_jit.py::TestModuleContainers::test_script_module_list_sequential, test/test_jit.py::TestModuleContainers::test_script_modulelist_index, test/test_jit.py::TestModuleContainers::test_sequential_intermediary_types, test/test_jit.py::TestModuleContainers::test_special_method_with_override, test/test_jit.py::TestModuleContainers::test_typed_module_dict, test/test_jit.py::TestModuleContainers::test_typed_module_list, test/test_jit.py::TestPythonBindings::test_add_input, test/test_jit.py::TestPythonBindings::test_aliasdb, test/test_jit.py::TestPythonBindings::test_canonicalize, test/test_jit.py::TestPythonBindings::test_cu_create_function, test/test_jit.py::TestPythonBindings::test_cu_get_functions, test/test_jit.py::TestPythonBindings::test_graph_create, test/test_jit.py::TestPythonBindings::test_graph_iterator_keepalive, test/test_jit.py::TestPythonBindings::test_invalidation, test/test_jit.py::TestPythonIr::test_param_strides, test/test_jit.py::TestPythonIr::test_permute_inputs_binding, test/test_jit.py::TestPythonIr::test_python_ir_utils, test/test_jit.py::TestPythonIr::test_python_ir_utils_graph, test/test_jit.py::TestFunctionalBlocks::test_subgraph_creation, test/test_jit.py::TestRemoveMutation::test_aten_inplace, test/test_jit.py::TestRemoveMutation::test_common_pytorch_list_ops, test/test_jit.py::TestRemoveMutation::test_if_output, test/test_jit.py::TestRemoveMutation::test_if_output_fail, test/test_jit.py::TestRemoveMutation::test_list_indexing_removal, test/test_jit.py::TestRemoveMutation::test_lists_append, test/test_jit.py::TestRemoveMutation::test_lists_insert, test/test_jit.py::TestRemoveMutation::test_special_mapped_op, test/test_jit.py::TestTorchbind::test_default_args, test/test_jit.py::TestTorchbind::test_hasattr, test/test_jit.py::TestTorchbind::test_lambda_as_constructor, test/test_jit.py::TestTorchbind::test_profiler_custom_op, test/test_jit.py::TestTorchbind::test_staticmethod, test/test_jit.py::TestTorchbind::test_torchbind, test/test_jit.py::TestTorchbind::test_torchbind_attr_exception, test/test_jit.py::TestTorchbind::test_torchbind_class_attr_recursive, test/test_jit.py::TestTorchbind::test_torchbind_class_attribute, test/test_jit.py::TestTorchbind::test_torchbind_deepcopy, test/test_jit.py::TestTorchbind::test_torchbind_def_property_getter_setter, test/test_jit.py::TestTorchbind::test_torchbind_def_property_just_getter, test/test_jit.py::TestTorchbind::test_torchbind_def_property_readwrite, test/test_jit.py::TestTorchbind::test_torchbind_getattr, test/test_jit.py::TestTorchbind::test_torchbind_getstate, test/test_jit.py::TestTorchbind::test_torchbind_instantiate_missing_class, test/test_jit.py::TestTorchbind::test_torchbind_lambda_method, test/test_jit.py::TestTorchbind::test_torchbind_no_init, test/test_jit.py::TestTorchbind::test_torchbind_optional_explicit_attr, test/test_jit.py::TestTorchbind::test_torchbind_pass_wrong_type, test/test_jit.py::TestTorchbind::test_torchbind_pickle_serialization, test/test_jit.py::TestTorchbind::test_torchbind_python_deepcopy, test/test_jit.py::TestTorchbind::test_torchbind_return_instance, test/test_jit.py::TestTorchbind::test_torchbind_return_instance_from_method, test/test_jit.py::TestTorchbind::test_torchbind_return_tuple, test/test_jit.py::TestTorchbind::test_torchbind_save_load, test/test_jit.py::TestTorchbind::test_torchbind_take_as_arg, test/test_jit.py::TestTorchbind::test_torchbind_take_instance_as_method_arg, test/test_jit.py::TestTorchbind::test_torchbind_tracing, test/test_jit.py::TestTorchbind::test_torchbind_tracing_nested, test/test_jit.py::TestModuleInterface::test_freeze_module_with_inplace_mutation_in_interface, test/test_jit.py::TestModuleInterface::test_freeze_module_with_interface, test/test_jit.py::TestModuleInterface::test_freeze_module_with_interface_and_fork, test/test_jit.py::TestModuleInterface::test_freeze_module_with_mutated_interface, test/test_jit.py::TestModuleInterface::test_freeze_module_with_setattr_in_interface, test/test_jit.py::TestModuleInterface::test_module_apis_interface, test/test_jit.py::TestModuleInterface::test_module_doc_string, test/test_jit.py::TestModuleInterface::test_module_interface, test/test_jit.py::TestModuleInterface::test_module_interface_inheritance, test/test_jit.py::TestModuleInterface::test_module_interface_subtype, test/test_jit.py::TestModuleInterface::test_module_swap, test/test_jit.py::TestModuleInterface::test_module_swap_no_lazy_compile, test/test_jit.py::TestModuleInterface::test_module_swap_no_module_interface, test/test_jit.py::TestModuleInterface::test_module_swap_wrong_module, test/test_jit.py::TestModuleInterface::test_not_submodule_interface_call, test/test_jit.py::TestModuleInterface::test_script_module_as_interface_swap, test/test_jit.py::TestWith::test_with_as, test/test_jit.py::TestWith::test_with_errors, test/test_jit.py::TestWith::test_with_exceptions, test/test_jit.py::TestWith::test_with_no_as, test/test_jit.py::TestWith::test_with_no_grad, test/test_jit.py::TestWith::test_with_record_function, test/test_jit.py::TestEnum::test_closed_over_enum_constant, test/test_jit.py::TestEnum::test_enum_as_const, test/test_jit.py::TestEnum::test_enum_as_module_attribute, test/test_jit.py::TestEnum::test_enum_comp, test/test_jit.py::TestEnum::test_enum_comp_diff_classes, test/test_jit.py::TestEnum::test_enum_explicit_script, test/test_jit.py::TestEnum::test_enum_iterate, test/test_jit.py::TestEnum::test_enum_ivalue_type, test/test_jit.py::TestEnum::test_enum_module_return, test/test_jit.py::TestEnum::test_enum_name, test/test_jit.py::TestEnum::test_enum_return, test/test_jit.py::TestEnum::test_enum_value, test/test_jit.py::TestEnum::test_enum_value_types, test/test_jit.py::TestEnum::test_heterogenous_value_type_enum_error, test/test_jit.py::TestEnum::test_non_existent_enum_value, test/test_jit.py::TestEnum::test_string_enum_as_module_attribute, test/test_jit.py::TestEnum::test_typed_enum, test/test_jit.py::TestStringFormatting::test_modulo_operator, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_alternate_digit_placeholder, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_capital_exponent_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_char_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_true_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_digit_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_digit_placeholder_and_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_double_percent_in_string, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_exponent_placeholder_and_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_float_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_float_placeholder_and_float_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_lowercase_exponent_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_multiple_placeholders, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_percent_in_string, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_format_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_subscript, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_too_few_arguments, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_too_many_arguments, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_unknown_format_specifier, test/test_jit.py::TestProfiler::test_aliasing_merge, test/test_jit.py::TestProfiler::test_autograd_fallback_graph, test/test_jit.py::TestProfiler::test_fallback_graph_not_specialized, test/test_jit.py::TestProfiler::test_iterative_fusion, test/test_jit.py::TestProfiler::test_local_fusion_strategy, test/test_jit.py::TestProfiler::test_not_fusing_scalar_ops, test/test_jit.py::TestProfiler::test_not_optimizing_property, test/test_jit.py::TestProfiler::test_specialize_backward, test/test_jit.py::TestProfiler::test_specialized_types, test/test_jit.py::TestProfiler::test_tensor_constant, test/test_jit.py::TestProfiler::test_tensor_type_not_determined_by_inputs, test/test_jit.py::TestProfiler::test_use_not_profiled, test/test_jit.py::TestSlice::test_module_list_slicing, test/test_jit.py::TestSlice::test_slice_as_variable, test/test_jit.py::TestSlice::test_slice_dynamic_index, test/test_jit.py::TestSlice::test_slice_kwarg, test/test_jit.py::TestSlice::test_slice_one_none, test/test_jit.py::TestSlice::test_slice_start_stop, test/test_jit.py::TestSlice::test_slice_start_stop_step, test/test_jit.py::TestSlice::test_slice_start_stop_with_none, test/test_jit.py::TestSlice::test_slice_stop_clipped, test/test_jit.py::TestSlice::test_slice_stop_only, test/test_jit.py::TestSlice::test_slice_stop_only_with_nones, test/test_jit.py::TestSlice::test_slice_string, test/test_jit.py::TestSlice::test_slice_tensor, test/test_jit.py::TestSlice::test_slice_tensor_multidim, test/test_jit.py::TestSlice::test_slice_tensor_multidim_with_dots, test/test_jit.py::TestSlice::test_slice_three_nones, test/test_jit.py::TestSlice::test_slice_two_nones, test/test_jit.py::TestSlice::test_tuple_slicing, test/test_jit.py::TestIgnorableArgs::test_add_out_ignorable_args, test/test_jit.py::TestIgnorableArgs::test_slice_ignorable_args_for_slice, test/test_jit.py::TestHooks::test_forward_tuple_input, test/test_jit.py::TestHooks::test_hook_compilation_hint, test/test_jit.py::TestHooks::test_hook_hook_name_collision, test/test_jit.py::TestHooks::test_hook_method_name_collision, test/test_jit.py::TestHooks::test_module_direct_forward_invocation, test/test_jit.py::TestHooks::test_module_forward_multiple_inputs, test/test_jit.py::TestHooks::test_module_forward_single_input, test/test_jit.py::TestHooks::test_module_hook_return_nothing, test/test_jit.py::TestHooks::test_module_multiple_hooks_multiple_inputs, test/test_jit.py::TestHooks::test_module_multiple_hooks_single_input, test/test_jit.py::TestHooks::test_module_no_forward_input, test/test_jit.py::TestHooks::test_module_same_hook_repeated, test/test_jit.py::TestHooks::test_submodule_called_directly_with_hooks, test/test_jit.py::TestHooks::test_submodule_direct_forward_invocation, test/test_jit.py::TestHooks::test_submodule_forward_multiple_inputs, test/test_jit.py::TestHooks::test_submodule_forward_single_input, test/test_jit.py::TestHooks::test_submodule_forward_single_input_return_not_tupled, test/test_jit.py::TestHooks::test_submodule_hook_return_nothing, test/test_jit.py::TestHooks::test_submodule_multiple_hooks_multiple_inputs, test/test_jit.py::TestHooks::test_submodule_multiple_hooks_single_input, test/test_jit.py::TestHooks::test_submodule_no_forward_input, test/test_jit.py::TestHooks::test_submodule_same_hook_repeated, test/test_jit.py::TestHooks::test_wrong_hook_signatures, test/test_jit.py::TestHooks::test_wrong_pre_hook_signatures, test/test_jit.py::TestWarn::test_warn, test/test_jit.py::TestWarn::test_warn_multiple_calls_multiple_warnings, test/test_jit.py::TestWarn::test_warn_multiple_calls_same_func_diff_stack, test/test_jit.py::TestWarn::test_warn_once_per_func, test/test_jit.py::TestWarn::test_warn_once_per_func_in_loop, test/test_jit.py::TestWarn::test_warn_only_once, test/test_jit.py::TestWarn::test_warn_only_once_in_loop_func, test/test_jit.py::TestIsinstance::test_bool, test/test_jit.py::TestIsinstance::test_dict, test/test_jit.py::TestIsinstance::test_dict_nested, test/test_jit.py::TestIsinstance::test_dict_no_contained_type, test/test_jit.py::TestIsinstance::test_dict_tensor, test/test_jit.py::TestIsinstance::test_empty_container_special_cases, test/test_jit.py::TestIsinstance::test_empty_container_throws_warning_in_eager, test/test_jit.py::TestIsinstance::test_float, test/test_jit.py::TestIsinstance::test_if_else, test/test_jit.py::TestIsinstance::test_in_if, test/test_jit.py::TestIsinstance::test_in_while_loop, test/test_jit.py::TestIsinstance::test_int, test/test_jit.py::TestIsinstance::test_list, test/test_jit.py::TestIsinstance::test_list_nested, test/test_jit.py::TestIsinstance::test_list_no_contained_type, test/test_jit.py::TestIsinstance::test_list_tensor, test/test_jit.py::TestIsinstance::test_list_tensor_type_true, test/test_jit.py::TestIsinstance::test_nontuple_container_rhs_throws_in_eager, test/test_jit.py::TestIsinstance::test_optional, test/test_jit.py::TestIsinstance::test_optional_nested, test/test_jit.py::TestIsinstance::test_optional_no_contained_type, test/test_jit.py::TestIsinstance::test_optional_none, test/test_jit.py::TestIsinstance::test_tensor_type_false, test/test_jit.py::TestIsinstance::test_tuple, test/test_jit.py::TestIsinstance::test_tuple_nested, test/test_jit.py::TestIsinstance::test_tuple_no_contained_type, test/test_jit.py::TestIsinstance::test_tuple_rhs, test/test_jit.py::TestIsinstance::test_tuple_tensor, test/test_jit.py::TestIsinstance::test_type_refinement, test/test_jit.py::TestPythonBuiltinOP::test_add, test/test_jit.py::TestPythonBuiltinOP::test_adv_indexing_list, test/test_jit.py::TestPythonBuiltinOP::test_advancedindex, test/test_jit.py::TestPythonBuiltinOP::test_gather, test/test_jit.py::TestPythonBuiltinOP::test_index, test/test_jit.py::TestPythonBuiltinOP::test_index_ellipses, test/test_jit.py::TestPythonBuiltinOP::test_inf, test/test_jit.py::TestPythonBuiltinOP::test_matmul_py3, test/test_jit.py::TestPythonBuiltinOP::test_mul, test/test_jit.py::TestPythonBuiltinOP::test_pow, test/test_jit.py::TestPythonBuiltinOP::test_random, test/test_jit.py::TestPythonBuiltinOP::test_slice, test/test_jit.py::TestPythonBuiltinOP::test_stepped_tuple_slicing, test/test_jit.py::TestPythonBuiltinOP::test_str_to_float, test/test_jit.py::TestPythonBuiltinOP::test_triple, test/test_jit.py::TestTyping::test_bool_list_io, test/test_jit.py::TestTyping::test_dict_comprehension, test/test_jit.py::TestTyping::test_dict_comprehension_scope, test/test_jit.py::TestTyping::test_dict_comprehension_with_type_annotation, test/test_jit.py::TestTyping::test_dict_in_not_in, test/test_jit.py::TestTyping::test_dict_invalid_annotations, test/test_jit.py::TestTyping::test_dict_type_refinement_annotation_key_mismatch, test/test_jit.py::TestTyping::test_dict_type_refinement_annotation_value_mismatch, test/test_jit.py::TestTyping::test_for_in_dict, test/test_jit.py::TestTyping::test_for_in_string, test/test_jit.py::TestTyping::test_for_tuple_assign, test/test_jit.py::TestTyping::test_for_tuple_unpack, test/test_jit.py::TestTyping::test_inherited_annotations_python_310, test/test_jit.py::TestTyping::test_list_io, test/test_jit.py::TestTyping::test_list_iterables, test/test_jit.py::TestTyping::test_list_sum, test/test_jit.py::TestTyping::test_list_type_refinement_annotation_element_mismatch, test/test_jit.py::TestTyping::test_list_unification, test/test_jit.py::TestTyping::test_multiple_assign, test/test_jit.py::TestTyping::test_namedtuple_error_source_attribution, test/test_jit.py::TestTyping::test_namedtuple_good_error, test/test_jit.py::TestTyping::test_namedtuple_py2, test/test_jit.py::TestTyping::test_namedtuple_redefine, test/test_jit.py::TestTyping::test_nested_list, test/test_jit.py::TestTyping::test_opt_opt_refinement, test/test_jit.py::TestTyping::test_optional_conversion, test/test_jit.py::TestTyping::test_optional_refinement, test/test_jit.py::TestTyping::test_optional_tuple, test/test_jit.py::TestTyping::test_singleton_tuple_unpack, test/test_jit.py::TestTyping::test_sum_list_diff_elms, test/test_jit.py::TestTyping::test_sum_list_empty, test/test_jit.py::TestTyping::test_sum_list_literal, test/test_jit.py::TestTyping::test_sum_list_one, test/test_jit.py::TestTyping::test_sum_list_wrong_type, test/test_jit.py::TestTyping::test_tuple_assignments, test/test_jit.py::TestTyping::test_tuple_create_return, test/test_jit.py::TestTyping::test_tuple_io, test/test_jit.py::TestTyping::test_tuple_keyword, test/test_jit.py::TestTyping::test_tuple_specialization, test/test_jit.py::TestHash::test_hash_bool, test/test_jit.py::TestHash::test_hash_device, test/test_jit.py::TestHash::test_hash_float, test/test_jit.py::TestHash::test_hash_int, test/test_jit.py::TestHash::test_hash_none, test/test_jit.py::TestHash::test_hash_string, test/test_jit.py::TestHash::test_hash_tensor, test/test_jit.py::TestHash::test_hash_tuple, test/test_jit.py::TestHash::test_hash_tuple_nested_unhashable_type, test/test_jit.py::TestComplex::test_binary_op_complex_tensor, test/test_jit.py::TestComplex::test_comparison_ops, test/test_jit.py::TestComplex::test_complex_constants_and_ops, test/test_jit.py::TestComplex::test_complex_constructor, test/test_jit.py::TestComplex::test_complex_list_sum, test/test_jit.py::TestComplex::test_complex_parse, test/test_jit.py::TestComplex::test_complexdict, test/test_jit.py::TestComplex::test_complexlist, test/test_jit.py::TestComplex::test_div, test/test_jit.py::TestComplex::test_infj_nanj_pickle, test/test_jit.py::TestComplex::test_pickle, test/test_jit.py::TestComplex::test_script, test/test_jit.py::TestComplex::test_tensor_attributes, test/test_jit.py::TestComplex::test_torch_complex_constructor_with_tensor, test/test_jit.py::TestJitUtils::test_checkscriptassertraisesregex, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_hybrid, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_keyword_only, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_positional_only, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_positional_or_keyword, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_var_keyword, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_var_positional, test/test_jit.py::TestJitUtils::test_no_tracer_warn_context_manager, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_class_level_annotation_and_init_annotation, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_class_level_annotation_only, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_class_level_jit_annotation, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_dict, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_dict_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_list, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_list_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_optional, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_tensor, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_falsy_base_type, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_nonempty_container, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_attribute, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_dict, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_dict_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_list, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_list_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_optional, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_torch_jit_import, test/test_jit.py::TestTypesAndAnnotation::test_annotate_outside_init, test/test_jit.py::TestTypesAndAnnotation::test_bad_types, test/test_jit.py::TestTypesAndAnnotation::test_ignore_with_types, test/test_jit.py::TestTypesAndAnnotation::test_ignoring_fn_with_nonscriptable_types, test/test_jit.py::TestTypesAndAnnotation::test_ignoring_module_attributes, test/test_jit.py::TestTypesAndAnnotation::test_inferred_type_error_message, test/test_jit.py::TestTypesAndAnnotation::test_mismatched_annotation, test/test_jit.py::TestTypesAndAnnotation::test_optional_no_element_type_annotation, test/test_jit.py::TestTypesAndAnnotation::test_parser_bug, test/test_jit.py::TestTypesAndAnnotation::test_pep585_type, test/test_jit.py::TestTypesAndAnnotation::test_python_callable, test/test_jit.py::TestTypesAndAnnotation::test_reannotate, test/test_jit.py::TestTypesAndAnnotation::test_tuple_no_element_type_annotation, test/test_jit.py::TestTypesAndAnnotation::test_type_annotate_py3, test/test_jit.py::TestTypesAndAnnotation::test_types_as_values, test/test_jit.py::TestTypesAndAnnotation::test_unimported_type_resolution, test/test_jit.py::TestMisc::test_broadcasting_list, test/test_jit.py::TestMisc::test_export_opnames_interface, test/test_jit.py::TestMisc::test_future_isinstance, test/test_jit.py::TestMisc::test_hacked_twin, test/test_jit.py::TestMisc::test_if_returning_any, test/test_jit.py::TestMisc::test_jit_get_operation_order, test/test_jit.py::TestMisc::test_joined_str, test/test_jit.py::TestMisc::test_kwarg_support, test/test_jit.py::TestMisc::test_legacy_tensor_constructor, test/test_jit.py::TestMisc::test_list_literal_infer, test/test_jit.py::TestMisc::test_math_inf, test/test_jit.py::TestMisc::test_parse_ir_annotate, test/test_jit.py::TestMisc::test_parse_ir_single_element_tensor_negative, test/test_jit.py::TestMisc::test_parse_ir_single_element_tensor_positive, test/test_jit.py::TestMisc::test_pow_multiple_dtype, test/test_jit.py::TestMisc::test_script_many_decorators, test/test_jit.py::TestMisc::test_str_refine_any, test/test_jit.py::TestMisc::test_subexpression_Dict_int_Future, test/test_jit.py::TestMisc::test_subexpression_Future_annotate, test/test_jit.py::TestMisc::test_subexpression_List_Future, test/test_jit.py::TestMisc::test_subexpression_Optional, test/test_jit.py::TestMisc::test_subexpression_Tuple_int_int_Future, test/test_jit.py::TestMisc::test_tuple_subscripted_assign, test/test_jit.py::TestMisc::test_unsafe_hacked_twin, test/test_jit.py::TestUpgraders::test_add_value_to_version_map, test/test_jit.py::TestUpgraders::test_aten_div_scalar_at_3, test/test_jit.py::TestUpgraders::test_aten_div_tensor_at_3, test/test_jit.py::TestUpgraders::test_aten_div_tensor_out_at_3, test/test_jit.py::TestUpgraders::test_aten_full_at_4, test/test_jit.py::TestUpgraders::test_aten_full_other_variants, test/test_jit.py::TestUpgraders::test_aten_full_out_at_4, test/test_jit.py::TestUpgraders::test_aten_linspace, test/test_jit.py::TestUpgraders::test_aten_linspace_out, test/test_jit.py::TestUpgraders::test_aten_logspace, test/test_jit.py::TestUpgraders::test_aten_logspace_out, test/test_jit.py::TestUpgraders::test_aten_test_serialization, test/test_jit.py::TestUpgraders::test_populated_test_upgrader_graph, test/test_jit.py::TestUpgraders::test_populated_upgrader_graph, test/test_jit.py::TestTensorCreationOps::test_randperm_default_dtype, test/test_jit.py::TestTensorCreationOps::test_randperm_specifed_dtype, test/test_jit.py::TestTensorCreationOps::test_tril_indices_default_dtype, test/test_jit.py::TestTensorCreationOps::test_tril_indices_specified_dtype, test/test_jit.py::TestTensorCreationOps::test_triu_indices_default_dtype, test/test_jit.py::TestTensorCreationOps::test_triu_indices_specified_dtype, test/test_jit.py::TestModuleAPIs::test_customized_state_dict_methods, test/test_jit.py::TestModuleAPIs::test_default_state_dict_methods, test/test_jit.py::TestModuleAPIs::test_submodule_customized_state_dict_methods, test/test_jit.py::TestScriptProfile::test_basic, test/test_jit.py::TestScriptProfile::test_empty, test/test_jit.py::TestScriptProfile::test_multi, test/test_jit.py::TestScriptProfile::test_script, test/test_jit.py::TestScriptProfile::test_section, test/test_jit.py::TestFunctionalToInplaceActivation::test_check_no_type_promotion, test/test_jit.py::TestFunctionalToInplaceActivation::test_functional_to_inplace_activation, test/test_jit.py::TestFunctionalToInplaceActivation::test_no_functional_to_inplace, test/test_jit.py::TestFunctionalToInplaceActivation::test_resnet18_correctness, test/test_jit.py::TestInplaceToFunctionalActivation::test_inplace_to_functional_activation, test/test_jit.py::TestInplaceToFunctionalActivation::test_resnet18_correctness, test/test_jit.py::TestParametrization::test_scriptable, test/test_jit.py::TestParametrization::test_traceable, test/test_jit.py::TestGetDefaultAttr::test_getattr_named_tuple, test/test_jit.py::TestGetDefaultAttr::test_getattr_tuple, test/test_jit.py::TestGetDefaultAttr::test_getattr_with_default, test/test_jit.py::TestAtenPow::test_aten_pow_zero_negative_exponent, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_fuse_activation_with_pack_ops_linear_conv2d_1, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_fuse_activation_with_pack_ops_linear_conv2d_2, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_fuse_activation_with_pack_ops_linear_conv2d_3, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_fuse_activation_with_pack_ops_linear_conv2d_4, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_insert_pre_packed_linear_before_inline_and_conv_2d_op, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_insert_pre_packed_linear_op, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_replace_conv1d_with_conv2d, test/test_jit.py::TestUnion::test_check_union_annotation, test/test_jit.py::TestUnion::test_union_T_None_is_equivalent_to_optional_T, test/test_jit.py::TestUnion::test_union_argument_order_is_ignored, test/test_jit.py::TestUnion::test_union_argument_order_is_ignored_container, test/test_jit.py::TestUnion::test_union_as_annotation, test/test_jit.py::TestUnion::test_union_as_annotation_in_typed_container, test/test_jit.py::TestUnion::test_union_as_annotation_py2, test/test_jit.py::TestUnion::test_union_as_dict_key, test/test_jit.py::TestUnion::test_union_as_dict_value, test/test_jit.py::TestUnion::test_union_as_internal_tuple_type, test/test_jit.py::TestUnion::test_union_branching_does_not_autoinfer_undeclared_union, test/test_jit.py::TestUnion::test_union_branching_does_not_widen_existing_inferred_type, test/test_jit.py::TestUnion::test_union_branching_with_union_return_and_homogenous_types, test/test_jit.py::TestUnion::test_union_does_not_replace_existing_annotated_type, test/test_jit.py::TestUnion::test_union_does_not_replace_existing_annotated_type_empty_container, test/test_jit.py::TestUnion::test_union_does_not_replace_existing_annotated_type_union, test/test_jit.py::TestUnion::test_union_in_class_constructor, test/test_jit.py::TestUnion::test_union_memory_aliasing, test/test_jit.py::TestUnion::test_union_module_with_union_class_variable, test/test_jit.py::TestUnion::test_union_module_with_union_instance_variable, test/test_jit.py::TestUnion::test_union_optional_of_union_is_flattened, test/test_jit.py::TestUnion::test_union_redundant_arguments_are_skipped, test/test_jit.py::TestUnion::test_union_redundant_arguments_are_skipped_container, test/test_jit.py::TestUnion::test_union_redundant_arguments_are_skipped_optional, test/test_jit.py::TestUnion::test_union_redundant_arguments_are_skipped_subtyping, test/test_jit.py::TestUnion::test_union_return_type, test/test_jit.py::TestUnion::test_union_schema_matching_on_internal_type, test/test_jit.py::TestUnion::test_union_serialization_preserves_type_annotations, test/test_jit.py::TestUnion::test_union_subclasses_larger_union, test/test_jit.py::TestUnion::test_union_subtractive_refinement, test/test_jit.py::TestUnion::test_union_subtractive_refinement_with_container, test/test_jit.py::TestUnion::test_union_type_refinement, test/test_jit.py::TestUnion::test_union_type_refinement_internal_declaration, test/test_jit.py::TestUnion::test_union_type_refinement_partial_static_refinement_tuple_rhs, test/test_jit.py::TestUnion::test_union_type_refinement_partial_static_refinement_union_rhs, test/test_jit.py::TestUnion::test_union_type_refinement_statically_false, test/test_jit.py::TestUnion::test_union_type_refinement_statically_true, test/test_jit.py::TestUnion::test_union_type_refinement_tuple_rhs, test/test_jit.py::TestUnion::test_union_type_refinement_tuple_rhs_noncontained_type, test/test_jit.py::TestUnion::test_union_type_refinement_tuple_rhs_union, 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test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_nn_functional_prelu_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_nn_functional_prelu_cpu_float16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_nn_functional_prelu_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_nn_functional_prelu_cpu_float64, test/test_jit.py::TestDeviceAnalysis::test_custom_device_op, test/test_jit.py::TestDeviceAnalysis::test_device_apply, test/test_jit.py::TestDeviceAnalysis::test_device_arg, test/test_jit.py::TestDeviceAnalysis::test_device_if_propagation, test/test_jit.py::TestDeviceAnalysis::test_if_loop_mix, test/test_jit.py::TestDeviceAnalysis::test_loop_device_change, test/test_jit.py::TestDeviceAnalysis::test_loop_simple, test/test_jit.py::TestDeviceAnalysis::test_mobilenet, test/test_jit.py::TestDeviceAnalysis::test_nested_loops, test/test_jit.py::TestDeviceAnalysis::test_set_dtype, 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test/test_jit.py::TestScript::test_torch_functional_tensordot_tuple, test/test_jit.py::TestScript::test_torch_ignore_conversion_to_none, test/test_jit.py::TestScript::test_torch_manual_seed, test/test_jit.py::TestScript::test_torch_pow, test/test_jit.py::TestScript::test_torch_tensor_as_tensor, test/test_jit.py::TestScript::test_torch_tensor_as_tensor_empty_list, test/test_jit.py::TestScript::test_torch_tensor_bad_input, test/test_jit.py::TestScript::test_torch_tensor_dtype, test/test_jit.py::TestScript::test_torchscript_memoryformat, test/test_jit.py::TestScript::test_torchscript_multi_head_attn, test/test_jit.py::TestScript::test_torchscript_multi_head_attn_fast_path, test/test_jit.py::TestScript::test_training_param, test/test_jit.py::TestScript::test_tuple_assignments, test/test_jit.py::TestScript::test_tuple_error_msg, test/test_jit.py::TestScript::test_tuple_index_to_list, test/test_jit.py::TestScript::test_tuple_indexing, test/test_jit.py::TestScript::test_tuple_len, test/test_jit.py::TestScript::test_tuple_nested_sort, test/test_jit.py::TestScript::test_tuple_sort, test/test_jit.py::TestScript::test_tuple_sort_reverse, test/test_jit.py::TestScript::test_tuple_sorted, test/test_jit.py::TestScript::test_tuple_str, test/test_jit.py::TestScript::test_tuple_to_opt_list, test/test_jit.py::TestScript::test_tuple_unsortable_diff_type, test/test_jit.py::TestScript::test_tuple_unsortable_element_type, test/test_jit.py::TestScript::test_tuple_unsortable_nested_diff_type, test/test_jit.py::TestScript::test_type_annotate, test/test_jit.py::TestScript::test_type_annotation_module, test/test_jit.py::TestScript::test_type_annotation_py3, test/test_jit.py::TestScript::test_type_annotations, test/test_jit.py::TestScript::test_type_annotations_repeated_list, test/test_jit.py::TestScript::test_type_annotations_varargs, test/test_jit.py::TestScript::test_type_call_in_script, test/test_jit.py::TestScript::test_type_cast, test/test_jit.py::TestScript::test_type_comments_in_body, test/test_jit.py::TestScript::test_type_inferred_from_empty_annotation, test/test_jit.py::TestScript::test_unbind, test/test_jit.py::TestScript::test_unfold_zero_dim, test/test_jit.py::TestScript::test_unicode_comments, test/test_jit.py::TestScript::test_uninitialized, test/test_jit.py::TestScript::test_union_to_number, test/test_jit.py::TestScript::test_unknown_builtin, test/test_jit.py::TestScript::test_unmatched_type_annotation, test/test_jit.py::TestScript::test_unspecialized_any_binding, test/test_jit.py::TestScript::test_unsqueeze_guard_elimination, test/test_jit.py::TestScript::test_unsupported_builtin_error, test/test_jit.py::TestScript::test_unused_decorator, test/test_jit.py::TestScript::test_unwrap_optional_builtin, test/test_jit.py::TestScript::test_var_aug_assign, test/test_jit.py::TestScript::test_vararg_zeros, test/test_jit.py::TestScript::test_view_listconstruct_shape_prop, test/test_jit.py::TestScript::test_view_shape_prop, test/test_jit.py::TestScript::test_view_write, test/test_jit.py::TestScript::test_weak_cuda, test/test_jit.py::TestScript::test_where, test/test_jit.py::TestScript::test_where_method, test/test_jit.py::TestScript::test_while, test/test_jit.py::TestScript::test_while_nest_if, test/test_jit.py::TestScript::test_while_nonexistent_cond_value, test/test_jit.py::TestScript::test_while_nonexistent_value, test/test_jit.py::TestScript::test_while_write_outer_then_read, test/test_jit.py::TestScript::test_wrong_attr_lookup, test/test_jit.py::TestScript::test_wrong_implicit_expand, test/test_jit.py::TestScript::test_wrong_method_call_inputs, test/test_jit.py::TestScript::test_wrong_module_attr_lookup, test/test_jit.py::TestScript::test_wrong_return_type, test/test_jit.py::TestScript::test_wrong_use_as_callable, test/test_jit.py::TestScript::test_wrong_use_as_tuple, test/test_jit.py::TestScript::test_zero_dimension_tensor_trace, test/test_jit.py::TestScript::test_zeros, test/test_jit.py::TestScript::test_zip_enumerate_modulelist, test/test_jit.py::TestJitGeneratedModule::test_nn_BCELoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_BCELoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_BCELoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_BCELoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_BCELoss_no_reduce_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_BCELoss_weights_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_BCELoss_weights_no_reduce_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_BCEWithLogitsLoss_legacy_enum, test/test_jit.py::TestJitGeneratedModule::test_nn_BCEWithLogitsLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_BCEWithLogitsLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_BCEWithLogitsLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_BCEWithLogitsLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_BCEWithLogitsLoss_no_reduce_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_Bilinear, test/test_jit.py::TestJitGeneratedModule::test_nn_CELU_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_circular_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_groups, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad1, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad1size1, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad2size1, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad_same, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad_same2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad_same_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_pad_valid, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_reflect_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_replicate_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_stride, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_zero_batch, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_zeros_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_circular_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_padded, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_strided, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_with_multiplier, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_groups, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_groups_thnn, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_pad_same, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_pad_same_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_pad_valid, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_padding, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_reflect_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_replicate_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_strided, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_zero_batch, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_zeros_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_1x1x1_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_circular_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_dilated_strided, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_groups, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_pad_same, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_pad_same_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_pad_valid, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_replicate_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride_padding, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_zero_batch, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_zeros_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d_groups, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d_groups, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose3d, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose3d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_CosineEmbeddingLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_CosineEmbeddingLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_CosineEmbeddingLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_CrossMapLRN2d, test/test_jit.py::TestJitGeneratedModule::test_nn_ELU_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Embedding, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_discontiguous, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_max, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_max_padding_idx, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_mean_padding_idx, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_sparse, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_EmbeddingBag_sum_padding_idx, test/test_jit.py::TestJitGeneratedModule::test_nn_Embedding_discontiguous, test/test_jit.py::TestJitGeneratedModule::test_nn_Embedding_sparse, test/test_jit.py::TestJitGeneratedModule::test_nn_Flatten, test/test_jit.py::TestJitGeneratedModule::test_nn_Flatten_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Fold, test/test_jit.py::TestJitGeneratedModule::test_nn_Fold_int_input, test/test_jit.py::TestJitGeneratedModule::test_nn_Fold_no_batch_dim_input, test/test_jit.py::TestJitGeneratedModule::test_nn_Fold_no_batch_dim_int_input, test/test_jit.py::TestJitGeneratedModule::test_nn_GELU_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_GLU_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_GRUCell, test/test_jit.py::TestJitGeneratedModule::test_nn_Hardshrink_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Hardsigmoid_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Hardswish_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Hardtanh_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_HingeEmbeddingLoss_margin_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_HingeEmbeddingLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_HingeEmbeddingLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_HingeEmbeddingLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_HingeEmbeddingLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_HuberLoss_delta, test/test_jit.py::TestJitGeneratedModule::test_nn_HuberLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_HuberLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_HuberLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_no_reduce_log_target, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_no_reduce_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_no_reduce_scalar_log_target, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_with_log_target_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_KLDivLoss_with_target_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_L1Loss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_L1Loss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_L1Loss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_L1Loss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_L1Loss_no_reduce_complex, test/test_jit.py::TestJitGeneratedModule::test_nn_L1Loss_no_reduce_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_LSTMCell, test/test_jit.py::TestJitGeneratedModule::test_nn_LayerNorm_3d_no_affine_large_feature, test/test_jit.py::TestJitGeneratedModule::test_nn_LeakyReLU_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Linear, test/test_jit.py::TestJitGeneratedModule::test_nn_Linear_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Linear_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_LogSigmoid_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_MSELoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_MSELoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_MSELoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_MSELoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MSELoss_no_reduce_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_MarginRankingLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_MarginRankingLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_MarginRankingLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_Mish_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelMarginLoss_0d_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelMarginLoss_1d_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelMarginLoss_index_neg, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelMarginLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelMarginLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelMarginLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelMarginLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelSoftMarginLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiMarginLoss_1d_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiMarginLoss_margin_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiMarginLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiMarginLoss_p_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiMarginLoss_weights_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_MultiheadAttention, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss2d_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss2d_no_reduce_ignore_index, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss2d_no_reduce_weights, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLossNd_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLossNd_no_reduce_ignore_index, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLossNd_no_reduce_weights, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_reduce_ignore_index, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_reduce_weights, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_reduce_weights_ignore_index, test/test_jit.py::TestJitGeneratedModule::test_nn_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_jit.py::TestJitGeneratedModule::test_nn_PReLU_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_PairwiseDistance, test/test_jit.py::TestJitGeneratedModule::test_nn_PairwiseDistance_broadcast_lhs, test/test_jit.py::TestJitGeneratedModule::test_nn_PairwiseDistance_broadcast_rhs, test/test_jit.py::TestJitGeneratedModule::test_nn_PairwiseDistance_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_PairwiseDistance_with_non_default_args, test/test_jit.py::TestJitGeneratedModule::test_nn_PixelShuffle, test/test_jit.py::TestJitGeneratedModule::test_nn_PixelUnshuffle, test/test_jit.py::TestJitGeneratedModule::test_nn_PoissonNLLLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_PoissonNLLLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_PoissonNLLLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_PoissonNLLLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_RNNCell, test/test_jit.py::TestJitGeneratedModule::test_nn_RReLU, 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2025-03-14T05:52:06.8960919Z 
2025-03-14T05:52:07.8449207Z Uploading artifacts took 1.14 seconds
2025-03-14T05:52:10.2822566Z Running test_serialization 1/1 ... [2025-03-14 05:52:10.281355]
2025-03-14T05:52:10.2823388Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:52:10.2825438Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_serialization.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:52:10.281765]
2025-03-14T05:52:43.9946483Z 
2025-03-14T05:52:43.9948241Z test_serialization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_serialization_1.1_b889b70354c86031_.log
2025-03-14T05:52:44.0114578Z Running 201 items in this shard: test/test_serialization.py::TestOldSerialization::test_debug_set_in_ci, test/test_serialization.py::TestOldSerialization::test_load_error_msg, test/test_serialization.py::TestOldSerialization::test_load_nonexistent_device, test/test_serialization.py::TestOldSerialization::test_load_python2_unicode_module, test/test_serialization.py::TestOldSerialization::test_load_unicode_error_msg, test/test_serialization.py::TestOldSerialization::test_pickle_module, test/test_serialization.py::TestOldSerialization::test_safe_load_basic_types, test/test_serialization.py::TestOldSerialization::test_save_different_dtype_error, test/test_serialization.py::TestOldSerialization::test_save_different_dtype_unallocated, test/test_serialization.py::TestOldSerialization::test_serialization, test/test_serialization.py::TestOldSerialization::test_serialization_backwards_compat, test/test_serialization.py::TestOldSerialization::test_serialization_backwards_compat_safe, test/test_serialization.py::TestOldSerialization::test_serialization_container, test/test_serialization.py::TestOldSerialization::test_serialization_container_filelike, test/test_serialization.py::TestOldSerialization::test_serialization_dill, test/test_serialization.py::TestOldSerialization::test_serialization_dill_version_not_supported, test/test_serialization.py::TestOldSerialization::test_serialization_fake_zip, test/test_serialization.py::TestOldSerialization::test_serialization_filelike, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_api_requirements, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_exceptions, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_missing_attrs, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_stress, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_uses_readinto, test/test_serialization.py::TestOldSerialization::test_serialization_gzip, test/test_serialization.py::TestOldSerialization::test_serialization_map_location, test/test_serialization.py::TestOldSerialization::test_serialization_offset, test/test_serialization.py::TestOldSerialization::test_serialization_offset_filelike_weights_only_False, test/test_serialization.py::TestOldSerialization::test_serialization_offset_filelike_weights_only_True, test/test_serialization.py::TestOldSerialization::test_serialization_offset_gzip, test/test_serialization.py::TestOldSerialization::test_serialization_safe, test/test_serialization.py::TestOldSerialization::test_serialization_save_warnings, test/test_serialization.py::TestOldSerialization::test_serialization_sparse, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_bsc_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_bsr_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_csc_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_csr_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_invalid_legacy_ctor, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_safe, test/test_serialization.py::TestOldSerialization::test_serialization_storage_slice, test/test_serialization.py::TestOldSerialization::test_serialization_zipfile_utils, test/test_serialization.py::TestOldSerialization::test_serialize_device, test/test_serialization.py::TestOldSerialization::test_skip_data_load, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_False_filename_False, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_False_filename_True, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_True_filename_False, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_True_filename_True, test/test_serialization.py::TestSerialization::test_debug_set_in_ci, test/test_serialization.py::TestSerialization::test_filewriter_metadata_writing_filename_False, test/test_serialization.py::TestSerialization::test_filewriter_metadata_writing_filename_True, test/test_serialization.py::TestSerialization::test_get_unsafe_globals_in_checkpoint, test/test_serialization.py::TestSerialization::test_has_format_version, test/test_serialization.py::TestSerialization::test_load_error_msg, test/test_serialization.py::TestSerialization::test_load_njt_weights_only_should_import_False, test/test_serialization.py::TestSerialization::test_load_njt_weights_only_should_import_True, test/test_serialization.py::TestSerialization::test_load_nonexistent_device, test/test_serialization.py::TestSerialization::test_load_python2_unicode_module, test/test_serialization.py::TestSerialization::test_load_unicode_error_msg, test/test_serialization.py::TestSerialization::test_lr_scheduler_serialization, test/test_serialization.py::TestSerialization::test_meta_serialization_weights_only_False, test/test_serialization.py::TestSerialization::test_meta_serialization_weights_only_True, test/test_serialization.py::TestSerialization::test_mmap_load_offset_calculation_path_type0, test/test_serialization.py::TestSerialization::test_mmap_load_offset_calculation_path_type1, test/test_serialization.py::TestSerialization::test_pathlike_serialization_weights_only_False, test/test_serialization.py::TestSerialization::test_pathlike_serialization_weights_only_True, test/test_serialization.py::TestSerialization::test_pickle_module, test/test_serialization.py::TestSerialization::test_safe_load_basic_types, test/test_serialization.py::TestSerialization::test_save_different_dtype_error, test/test_serialization.py::TestSerialization::test_save_different_dtype_unallocated, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bfloat16_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bfloat16_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bool_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bool_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex128_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex128_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex64_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex64_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float16_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float16_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float32_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float32_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float64_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float64_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int16_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int16_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int32_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int32_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int64_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int64_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int8_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int8_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_uint8_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_uint8_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization, test/test_serialization.py::TestSerialization::test_serialization_backwards_compat, test/test_serialization.py::TestSerialization::test_serialization_backwards_compat_safe, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals0_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals0_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals1_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals1_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_byteorder_mark, test/test_serialization.py::TestSerialization::test_serialization_dill, test/test_serialization.py::TestSerialization::test_serialization_dill_version_not_supported, test/test_serialization.py::TestSerialization::test_serialization_dtype_complex32_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_complex32_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e4m3fn_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e4m3fn_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e5m2_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e5m2_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint16_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint16_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint32_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint32_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint64_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint64_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_efficient_zerotensor_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_efficient_zerotensor_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_fake_zip, test/test_serialization.py::TestSerialization::test_serialization_filelike, test/test_serialization.py::TestSerialization::test_serialization_filelike_api_requirements, test/test_serialization.py::TestSerialization::test_serialization_filelike_exceptions, test/test_serialization.py::TestSerialization::test_serialization_filelike_missing_attrs, test/test_serialization.py::TestSerialization::test_serialization_filelike_stress, test/test_serialization.py::TestSerialization::test_serialization_filelike_uses_readinto, test/test_serialization.py::TestSerialization::test_serialization_gzip, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_bfloat16, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_bool, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_cdouble, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_cfloat, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_double, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_float, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_half, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_int, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_int16, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_int8, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_long, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_uint8, test/test_serialization.py::TestSerialization::test_serialization_map_location, test/test_serialization.py::TestSerialization::test_serialization_math_bits_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_math_bits_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_ctx, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type0_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type0_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type1_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type1_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_with_map_location, test/test_serialization.py::TestSerialization::test_serialization_nested_class, test/test_serialization.py::TestSerialization::test_serialization_offset_gzip, test/test_serialization.py::TestSerialization::test_serialization_python_attr_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_python_attr_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_safe, test/test_serialization.py::TestSerialization::test_serialization_save_warnings, test/test_serialization.py::TestSerialization::test_serialization_sparse, test/test_serialization.py::TestSerialization::test_serialization_sparse_bsc_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_bsr_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_csc_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_csr_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_invalid_legacy_ctor, test/test_serialization.py::TestSerialization::test_serialization_sparse_safe, test/test_serialization.py::TestSerialization::test_serialization_storage_slice, test/test_serialization.py::TestSerialization::test_serialization_uintx_intx, test/test_serialization.py::TestSerialization::test_serialization_warning_s390x, test/test_serialization.py::TestSerialization::test_serialization_with_header, test/test_serialization.py::TestSerialization::test_serialization_zipfile_actually_jit, test/test_serialization.py::TestSerialization::test_serialization_zipfile_utils, test/test_serialization.py::TestSerialization::test_serialization_zipfile_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_zipfile_weights_only_True, test/test_serialization.py::TestSerialization::test_serialize_device, test/test_serialization.py::TestSerialization::test_skip_data_load, test/test_serialization.py::TestSerialization::test_skip_data_serialization_error_cases, test/test_serialization.py::TestSerialization::test_skip_data_serialization_materialize_fake_False, test/test_serialization.py::TestSerialization::test_skip_data_serialization_materialize_fake_True, test/test_serialization.py::TestSerialization::test_skip_data_serialization_preserves_views_materialize_fake_False, test/test_serialization.py::TestSerialization::test_skip_data_serialization_preserves_views_materialize_fake_True, test/test_serialization.py::TestSerialization::test_storage_alignment, test/test_serialization.py::TestSerialization::test_use_pinned_memory_for_d2h, test/test_serialization.py::TestSerialization::test_weights_only_assert, test/test_serialization.py::TestSerialization::test_weights_only_env_variables_force_weights_only_False, test/test_serialization.py::TestSerialization::test_weights_only_env_variables_force_weights_only_True, test/test_serialization.py::TestSerialization::test_weights_only_error_unsafe_global_False, test/test_serialization.py::TestSerialization::test_weights_only_error_unsafe_global_True, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_blocklist, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_build, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_build_with_slots_slots_all, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_build_with_slots_slots_some, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_newobj, test/test_serialization.py::TestSubclassSerialization::test_cloned_deepcopy_requires_grad_False, test/test_serialization.py::TestSubclassSerialization::test_cloned_deepcopy_requires_grad_True, test/test_serialization.py::TestSubclassSerialization::test_empty_class_serialization, test/test_serialization.py::TestSubclassSerialization::test_safe_globals_context_manager_weights_only, test/test_serialization.py::TestSubclassSerialization::test_safe_globals_for_weights_only, test/test_serialization.py::TestSubclassSerialization::test_sets_are_loadable_with_weights_only, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_deepcopy, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_getstate_overwrite, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_map_location, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_wrapper_serialization, test/test_serialization.py::TestBothSerializationCPU::test_serialization_new_format_old_format_compat_weights_only_False_cpu, test/test_serialization.py::TestBothSerializationCPU::test_serialization_new_format_old_format_compat_weights_only_True_cpu
2025-03-14T05:52:44.0262613Z 
2025-03-14T05:52:48.4153205Z Running torch_np/numpy_tests/lib/test_twodim_base 1/1 ... [2025-03-14 05:52:48.414857]
2025-03-14T05:52:48.4154182Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:52:48.4156659Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_twodim_base.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:52:48.415310]
2025-03-14T05:52:53.5945736Z 
2025-03-14T05:52:53.5947461Z torch_np/numpy_tests/lib/test_twodim_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_twodim_base_1.1_d81f5dfe66c5a2b8_.log
2025-03-14T05:52:53.5970039Z Running 34 items in this shard: test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_2d, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_bool, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_diag, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_diag2d, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_eye_bounds, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_order, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_diag_bounds, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_failure, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_fortran_order, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_matrix, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_vector, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestFliplr::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestFlipud::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_all_outliers, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_asym, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_bad_length_x_len_10_y_len_11, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_bad_length_x_len_20_y_len_19, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_binparameter_combination, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_density, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_empty, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_simple, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_dtype, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_mask_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_dtype, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_ndim2, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_ndim3, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_with_inf, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTriuIndices::test_triu_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTrilIndicesFrom::test_exceptions, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTriuIndicesFrom::test_exceptions, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestVander::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestVander::test_dtypes
2025-03-14T05:52:53.5991409Z 
2025-03-14T05:52:57.6907794Z Running lazy/test_reuse_ir 1/1 ... [2025-03-14 05:52:57.690391]
2025-03-14T05:52:57.6908595Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:52:57.6910622Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_reuse_ir.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:52:57.690733]
2025-03-14T05:53:01.6684374Z 
2025-03-14T05:53:01.6685775Z lazy/test_reuse_ir 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_reuse_ir_1.1_3d20c02a4dcc2ae7_.log
2025-03-14T05:53:01.6689218Z Running 4 items in this shard: test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAdd, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAddSub, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAddSubFallback, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testBatchNorm
2025-03-14T05:53:01.6691353Z 
2025-03-14T05:53:05.2312770Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2025-03-14 05:53:05.230902]
2025-03-14T05:53:05.2313462Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:53:05.2314775Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:53:05.231218]
2025-03-14T05:53:09.5522184Z 
2025-03-14T05:53:09.5523957Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_1e071c6225f563c3_.log
2025-03-14T05:53:09.5554446Z Running 34 items in this shard: test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_both_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_nondtype_nonscalartype, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_same, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_sibling_class, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass_backwards, test/torch_np/numpy_tests/core/test_numerictypes.py::TestBitName::test_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestDocStrings::test_platform_dependent_aliases, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t9, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_unique, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t9
2025-03-14T05:53:09.5582650Z 
2025-03-14T05:53:13.9722851Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2025-03-14 05:53:13.971826]
2025-03-14T05:53:13.9723861Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:53:13.9726109Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_index_tricks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:53:13.972256]
2025-03-14T05:53:19.0445498Z 
2025-03-14T05:53:19.0448158Z torch_np/numpy_tests/lib/test_index_tricks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_85f00072c4ac024b_.log
2025-03-14T05:53:19.0483008Z Running 47 items in this shard: test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_big_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_clipmodes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_dtypes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_clip, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_raise, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_unravel, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_writeability, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_longdouble, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npcomplexfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_linspace_equivalence, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start0_stop_10_step0_expected0, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start_-10_stop_20_step1_expected1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_nd, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_sparse, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_1d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_2d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_complex_step, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_more_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdenumerate::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_simple_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_1d_only, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_bool, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_repeated_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_shape_and_dtype, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestC::test_c_, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_hetero_shape_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_low_dim_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_operate_4d_array, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_wide_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndices::test_diag_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_diag_indices_from, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_shape_mismatch, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_small_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdIndex::test_ndindex
2025-03-14T05:53:19.0516357Z 
2025-03-14T05:53:24.6088926Z Running test_type_info 1/1 ... [2025-03-14 05:53:24.608465]
2025-03-14T05:53:24.6089700Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:53:24.6094146Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_info.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:53:24.609069]
2025-03-14T05:53:29.8476227Z 
2025-03-14T05:53:29.8477539Z test_type_info 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_info_1.1_2fb8b9ec78c4f38e_.log
2025-03-14T05:53:29.8481046Z Running 5 items in this shard: test/test_type_info.py::TestDTypeInfo::test_finfo, test/test_type_info.py::TestDTypeInfo::test_iinfo, test/test_type_info.py::TestDTypeInfo::test_invalid_input, test/test_type_info.py::TestDTypeInfo::test_to_complex, test/test_type_info.py::TestDTypeInfo::test_to_real
2025-03-14T05:53:29.8483470Z 
2025-03-14T05:53:34.6350491Z Running functorch/test_vmap 1/1 ... [2025-03-14 05:53:34.634619]
2025-03-14T05:53:34.6351347Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:53:34.6354307Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:53:34.635091]
2025-03-14T05:54:26.4389196Z 
2025-03-14T05:54:26.4390510Z test_meta 2/4 was successful, full logs can be found in artifacts with path test/test-reports/test_meta_2.4_ac0d5edaeb43a05e_.log
2025-03-14T05:54:26.8182785Z Running 10200 items in this shard: test/test_meta.py::TestMetaConverter::test_channels_last, test/test_meta.py::TestMetaConverter::test_channels_last_leaf, test/test_meta.py::TestMetaConverter::test_view_dtype, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___rsub___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs__conversions_polar_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_clamp_min_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_div_trunc_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_floor_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_fmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_ge_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_igammac_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_logical_and_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_pow_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_true_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_clamp_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_copysign_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_div_no_rounding_mode_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_floor_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_fmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_ge_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_gt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_logical_or_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_maximum_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_chebyshev_polynomial_u_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_chebyshev_polynomial_v_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_legendre_polynomial_p_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_zeta_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_sub_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_xlogy_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rdiv___cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rdiv___cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rdiv___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmatmul___cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmul___cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmul___cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rmul___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rpow___cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rpow___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rpow___cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rpow___cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rsub___cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rxor___cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rxor___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___rxor___cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__batch_norm_with_update_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__chunk_cat_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__chunk_cat_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__chunk_cat_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__chunk_cat_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_abs_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_abs_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_abs_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_acos_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_acos_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_add_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_addcmul_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_asin_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_atan_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_atan_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_atan_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_max_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_max_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_min_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_min_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_clamp_min_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cos_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cosh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cosh_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_cosh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_div_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_div_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erf_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_erfc_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_exp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_exp_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_exp_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_expm1_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_floor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lerp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lerp_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lerp_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lerp_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lgamma_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lgamma_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lgamma_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_lgamma_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log10_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log1p_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log1p_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log1p_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log2_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log2_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log2_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_log_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_max_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_max_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_max_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_maximum_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_maximum_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_minimum_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_minimum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_minimum_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_minimum_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_mul_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_mul_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_mul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_neg_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_neg_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_neg_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_neg_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_pow_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_reciprocal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_reciprocal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_reciprocal_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_reciprocal_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_reciprocal_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_reciprocal_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_reciprocal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_round_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_round_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_round_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_round_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_round_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_rsqrt_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_rsqrt_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sigmoid_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sigmoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sigmoid_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sign_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sign_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sign_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sign_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sin_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sin_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sinh_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sinh_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sinh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sqrt_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sqrt_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sqrt_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sub_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sub_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sub_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_sub_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_tan_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_tan_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_tan_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_tanh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_tanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_tanh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_trunc_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_zero_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__foreach_zero_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__native_batch_norm_legit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__segment_reduce_lengths_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__softmax_backward_data_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__unsafe_masked_index_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__unsafe_masked_index_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__unsafe_masked_index_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__unsafe_masked_index_put_accumulate_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__unsafe_masked_index_put_accumulate_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace__upsample_bilinear2d_aa_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_abs_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_abs_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acos_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acos_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acos_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acosh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acosh_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_acosh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_add_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_add_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_add_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_add_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addbmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addcdiv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addcdiv_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addcmul_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addcmul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmm_decomposed_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmm_decomposed_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmm_decomposed_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmm_decomposed_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmv_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmv_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addmv_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addr_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_addr_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_alias_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_alias_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_alias_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_alias_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_alias_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_all_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_all_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_all_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_all_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_allclose_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amax_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amax_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_amin_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_aminmax_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_aminmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_aminmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_angle_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_any_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_arange_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_arange_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argmin_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argsort_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argsort_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argsort_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_argwhere_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_partial_views_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_partial_views_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_scatter_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_scatter_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_scatter_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_as_strided_scatter_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_asin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_asin_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_asin_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_asinh_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_asinh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_asinh_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_asinh_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atan2_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atan2_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atan2_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atan2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atan_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atan_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atan_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atanh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atanh_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atanh_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_1d_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_2d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_2d_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_2d_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_3d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_atleast_3d_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_baddbmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bernoulli_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bernoulli_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bfloat16_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bfloat16_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bfloat16_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bincount_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_left_shift_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_left_shift_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_left_shift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_not_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_not_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_not_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_or_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_or_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_or_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_right_shift_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_xor_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bitwise_xor_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_block_diag_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_block_diag_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bool_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bool_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bool_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bool_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bool_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bool_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_broadcast_tensors_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_broadcast_tensors_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_broadcast_to_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_broadcast_to_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_broadcast_to_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_broadcast_to_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_bucketize_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_byte_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_byte_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_byte_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_byte_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_byte_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cartesian_prod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cartesian_prod_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cartesian_prod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cartesian_prod_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cartesian_prod_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cat_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cat_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cat_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cauchy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cdouble_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cdouble_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_ceil_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_ceil_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_ceil_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cfloat_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cfloat_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chalf_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chalf_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_char_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_char_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_char_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_char_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cholesky_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cholesky_inverse_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cholesky_solve_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_cholesky_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chunk_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chunk_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_chunk_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_max_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_max_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_max_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_min_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clamp_min_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_clone_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_column_stack_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_combinations_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_combinations_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_complex_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_physical_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_physical_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_conj_physical_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_constant_pad_nd_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_constant_pad_nd_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_constant_pad_nd_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_contiguous_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_contiguous_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_contiguous_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_copysign_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_copysign_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_copysign_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_copysign_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_corrcoef_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_corrcoef_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_corrcoef_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_norm_subgradients_at_zero_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_pinv_singular_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_qr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_solve_ex_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_solve_ex_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_svd_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_svdvals_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_tensorinv_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_tensorinv_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_tensorsolve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_vander_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_vander_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_vecdot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linalg_vector_norm_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linspace_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linspace_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linspace_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linspace_tensor_overload_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_linspace_tensor_overload_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log10_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log10_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log10_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log1p_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log1p_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log1p_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log_normal_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log_softmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_log_softmax_cpu_float32, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_long_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_long_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_long_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_long_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_long_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_lt_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_mH_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_mH_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_mH_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_mT_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_mT_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_mT_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_mT_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_rsub_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_rsub_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_rsub_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scalar_tensor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scalar_tensor_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scalar_tensor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scatter_add_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scatter_add_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scatter_add_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scatter_add_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scatter_add_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_scatter_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cos_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cos_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cosh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cosh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_cosh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_div_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_div_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_div_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_div_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erf_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erf_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erf_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erf_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erfc_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erfc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_erfc_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_exp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_exp_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_expm1_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_expm1_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_floor_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_floor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_floor_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_frac_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_frac_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lerp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lerp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lerp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lgamma_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lgamma_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lgamma_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lgamma_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_lgamma_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log10_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log1p_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log1p_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log1p_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log1p_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log2_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_log2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_max_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_max_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_maximum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_maximum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_minimum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_minimum_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_mul_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_mul_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_mul_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_neg_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_neg_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_neg_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_norm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_norm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_norm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_pow_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_pow_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_pow_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_pow_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_pow_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_reciprocal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_reciprocal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_reciprocal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_round_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_round_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_rsqrt_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sigmoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sigmoid_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sigmoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sigmoid_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sub_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sub_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sub_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sub_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_trunc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_trunc_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_trunc_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_lengths_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_offsets_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__softmax_backward_data_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcdiv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcmul_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcmul_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcmul_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcmul_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcmul_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addcmul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_decomposed_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_allclose_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_allclose_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_allclose_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_amax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_amax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_amax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_angle_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_any_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_any_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_arange_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_arange_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_arange_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_arange_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_argmin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argsort_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_argsort_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_argsort_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_atan_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atanh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_1d_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_1d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_1d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_1d_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_atleast_3d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_baddbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_baddbmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_baddbmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bernoulli_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_left_shift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_left_shift_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_left_shift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_not_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_not_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_or_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_right_shift_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_xor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bucketize_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cauchy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdist_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_char_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_char_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cholesky_inverse_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_chunk_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_chunk_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_chunk_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_max_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_min_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_min_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_clamp_min_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_clone_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_column_stack_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_column_stack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_column_stack_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_combinations_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_physical_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_physical_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_conj_physical_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_constant_pad_nd_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cosh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cosh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cosh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_count_nonzero_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_count_nonzero_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cov_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cov_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cov_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cross_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cross_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cross_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummax_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cummin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumprod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumsum_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumulative_trapezoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cumulative_trapezoid_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_deg2rad_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_scatter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagonal_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diff_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diff_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_digamma_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_digamma_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_digamma_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dist_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_dist_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_floor_rounding_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_floor_rounding_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_floor_rounding_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_no_rounding_mode_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_div_trunc_rounding_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_dot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dot_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_dot_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_dot_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_double_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_double_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_double_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_double_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_double_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_dsplit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_dsplit_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_dsplit_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_dsplit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_dsplit_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_dstack_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_einsum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_einsum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_einsum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_like_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_permuted_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_strided_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_strided_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_empty_strided_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_eq_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_eq_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_eq_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_equal_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_equal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_equal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_equal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_erf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_erf_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_erf_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_erfc_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_erfinv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_exp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_as_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_as_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_expand_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_expm1_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_expm1_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_exponential_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_eye_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftshift_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftshift_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftshift_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_fftshift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft2_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_ldl_solve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_ldl_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_lu_factor_ex_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_lu_factor_ex_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_rank_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_rank_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_rank_hermitian_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_matrix_rank_hermitian_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_multi_dot_cpu_float32, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logaddexp2_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logaddexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logaddexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logaddexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logcumsumexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logcumsumexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logcumsumexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logdet_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logdet_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_and_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_not_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_not_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_or_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_or_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_or_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_or_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logical_xor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logit_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_logspace_tensor_overload_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_logsumexp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_solve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_unpack_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_unpack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logaddexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_median_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_std_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_std_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_std_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_std_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_sum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_sum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_var_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_var_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_matmul_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_matmul_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_matrix_exp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_matrix_exp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_binary_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_pool2d_with_indices_backward_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_no_dim_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_no_dim_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_no_dim_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_max_reduction_with_dim_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_maximum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_maximum_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_maximum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mean_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_mean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_median_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_median_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_median_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_median_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_list_of_tensors_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_list_of_tensors_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_variadic_tensors_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_variadic_tensors_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_variadic_tensors_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_variadic_tensors_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_meshgrid_variadic_tensors_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_binary_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_binary_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_binary_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_reduction_with_dim_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_reduction_with_dim_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_min_reduction_with_dim_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_minimum_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_minimum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_minimum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_minimum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_mm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_msort_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_msort_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mul_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_mul_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_mul_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mul_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mul_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_multinomial_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_multinomial_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_multinomial_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mv_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mv_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mv_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanquantile_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_layer_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nextafter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_alpha_dropout_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool3d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_batch_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_binary_cross_entropy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_conv1d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_conv2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_conv3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_conv_transpose1d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_conv_transpose3d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_cosine_embedding_loss_cpu_bool, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_interpolate_trilinear_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_kl_div_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_kl_div_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_l1_loss_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_l1_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_leaky_relu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_leaky_relu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_linear_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_linear_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_linear_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_logsigmoid_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_rot90_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_neg_3_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_int32, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_special_i1e_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_laguerre_polynomial_l_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_laguerre_polynomial_l_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_legendre_polynomial_p_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_legendre_polynomial_p_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_legendre_polynomial_p_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_log_ndtr_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_log_ndtr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_log_ndtr_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_log_ndtr_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_special_modified_bessel_i0_cpu_int32, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_split_list_args_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_list_args_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_with_sizes_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_with_sizes_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_with_sizes_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_with_sizes_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_with_sizes_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_with_sizes_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_split_with_sizes_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_sqrt_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_square_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_square_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_std_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_std_mean_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_std_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_std_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_stft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sub_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_sub_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_sum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_sum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_sum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_sum_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_sum_to_size_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_sum_to_size_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tensor_split_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_tensor_split_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tensor_split_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tensor_split_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tensor_split_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tensordot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tensordot_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_topk_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trace_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_triangular_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_indices_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_indices_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_uint16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_complex_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_vstack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_where_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_where_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_where_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_where_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_xlogy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_xlogy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_zero__cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_zero__cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_zero__cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_like_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_zeros_like_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_H_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_H_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_H_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_T_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_T_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_T_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_T_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_T_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace___getitem___cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace___getitem___cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace___radd___cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace___radd___cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace___rand___cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace___rand___cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace___rdiv___cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace___rdiv___cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace___rdiv___cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace___rdiv___cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmatmul___cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmatmul___cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmatmul___cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmod___cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmod___cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmul___cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmul___cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmul___cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmul___cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace___rmul___cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace___rpow___cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace___rpow___cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace___rpow___cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace___rpow___cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace___rsub___cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace___rxor___cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__batch_norm_with_update_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__batch_norm_with_update_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__batch_norm_with_update_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__chunk_cat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__chunk_cat_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__chunk_cat_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace__chunk_cat_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_abs_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_abs_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_abs_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_abs_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_abs_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_acos_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_acos_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_add_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_add_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_add_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_addcdiv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_addcdiv_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_addcdiv_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_addcmul_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_addcmul_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_addcmul_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_addcmul_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_asin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_asin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_asin_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_asin_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_atan_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_atan_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_ceil_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_clamp_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_clamp_min_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_clamp_min_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_cos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_cos_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_cos_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_cosh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_cosh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_cosh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_div_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_div_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_div_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_erf_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_erf_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_erf_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_erf_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_erf_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_erfc_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_erfc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_exp_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_exp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_exp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_exp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_exp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_exp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_expm1_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_expm1_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_floor_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_floor_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_floor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_frac_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_frac_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_frac_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_frac_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_frac_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_lerp_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_lerp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_lerp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_lerp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_lgamma_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_lgamma_cpu_complex64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_log_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_max_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_max_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_max_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_maximum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_maximum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_maximum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_minimum_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_minimum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_minimum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_mul_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_mul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_neg_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_neg_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_norm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_norm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_pow_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_pow_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_reciprocal_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_reciprocal_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_reciprocal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_reciprocal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_round_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_round_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_rsqrt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sigmoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sigmoid_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sigmoid_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sigmoid_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sigmoid_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sign_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sign_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sign_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sign_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sign_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sin_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sinh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sinh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sinh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sinh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sinh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sqrt_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sqrt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sqrt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sqrt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sqrt_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sqrt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sqrt_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sub_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_sub_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_tan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_tanh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_tanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_tanh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_tanh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_trunc_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_trunc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_trunc_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_trunc_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_trunc_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_zero_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_zero_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__foreach_zero_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__native_batch_norm_legit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__segment_reduce_offsets_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__softmax_backward_data_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__upsample_bilinear2d_aa_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__upsample_bilinear2d_aa_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argsort_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan2_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atanh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bincount_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bool_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_bool_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bool_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_shapes_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_tensors_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_tensors_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_tensors_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_tensors_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_to_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_to_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_broadcast_to_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bucketize_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bucketize_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bucketize_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_byte_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cartesian_prod_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cat_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cat_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cat_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cauchy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdouble_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdouble_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cdouble_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_ceil_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_ceil_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_ceil_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_ceil_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cfloat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cfloat_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_chalf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_chalf_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_chalf_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_chalf_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_chalf_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_char_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_char_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_char_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cholesky_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_chunk_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_chunk_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_max_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_max_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_max_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_max_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_min_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_clamp_min_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_clone_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_clone_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_column_stack_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_combinations_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_combinations_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_combinations_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_combinations_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_combinations_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_complex_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_conj_physical_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_constant_pad_nd_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_contiguous_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_contiguous_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_contiguous_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_copysign_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_copysign_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_copysign_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_copysign_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_copysign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_corrcoef_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_corrcoef_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_corrcoef_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_corrcoef_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_corrcoef_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_corrcoef_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cos_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cos_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cos_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cosh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_cosh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_count_nonzero_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cov_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cov_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cov_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cov_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cov_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cross_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_cross_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cross_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cummin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumprod_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumprod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumsum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumsum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumsum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumsum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumsum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumulative_trapezoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumulative_trapezoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumulative_trapezoid_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_cumulative_trapezoid_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_deg2rad_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_deg2rad_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diag_embed_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagflat_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diagonal_scatter_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_diff_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diff_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_diff_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_digamma_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_digamma_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_digamma_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_digamma_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_digamma_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_dist_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_dist_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_dist_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_trunc_rounding_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_trunc_rounding_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_trunc_rounding_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_dot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_dot_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_double_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_dsplit_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_dstack_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_dstack_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_dstack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_dstack_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_dstack_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_einsum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_einsum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_einsum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_like_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_like_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_like_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_permuted_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_permuted_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_strided_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_empty_strided_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_erf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_erf_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_erf_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfc_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfc_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_erfinv_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_exp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_as_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_as_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_as_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_as_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_as_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_expand_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_expm1_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_expm1_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_expm1_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_expm1_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_eye_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_eye_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_eye_cpu_float8_e5m2, test/test_meta.py::TestMetaCPU::test_meta_outplace_eye_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftn_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_fftshift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_hfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft2_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ifftshift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_ihfft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfft2_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_irfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_fft_rfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_fill_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_fill_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_flatten_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_flip_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_fliplr_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_flipud_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_float_power_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_divide_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_divide_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_floor_divide_cpu_int64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_bilinear_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_celu_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_celu_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_channel_shuffle_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_channel_shuffle_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_channel_shuffle_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv2d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose1d_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose3d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_conv_transpose3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_cosine_embedding_loss_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_cosine_similarity_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_cross_entropy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_ctc_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_dropout2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_dropout_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_dropout_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_elu_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_elu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_embedding_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_embedding_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_embedding_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_with_train_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_without_train_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_without_train_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_without_train_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_without_train_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_feature_alpha_dropout_without_train_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_fractional_max_pool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_fractional_max_pool2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_gaussian_nll_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_gaussian_nll_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_gaussian_nll_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_group_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardshrink_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardsigmoid_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardswish_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardswish_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_hardswish_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_huber_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_bilinear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_nearest-exact_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_nearest-exact_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_trilinear_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_interpolate_trilinear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_kl_div_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_kl_div_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_l1_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_l1_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_leaky_relu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_leaky_relu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_linear_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_linear_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_linear_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_linear_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool3d_grad_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool3d_grad_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_max_unpool3d_grad_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_mish_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_mish_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_mse_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_mse_loss_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_multi_head_attention_forward_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_multi_head_attention_forward_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_nll_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_normalize_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_normalize_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_circular_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_constant_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_reflect_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_reflect_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_reflect_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pad_replicate_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pairwise_distance_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pairwise_distance_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pdist_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_unshuffle_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_pixel_unshuffle_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_poisson_nll_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_poisson_nll_loss_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu6_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_relu_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_rms_norm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_rms_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_rrelu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_smooth_l1_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softmin_with_dtype_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softmin_with_dtype_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softmin_with_dtype_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softplus_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softplus_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softshrink_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softshrink_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softsign_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softsign_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softsign_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softsign_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_softsign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_tanhshrink_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_tanhshrink_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_tanhshrink_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_threshold_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_threshold_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_threshold_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_loss_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_loss_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_with_distance_loss_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_with_distance_loss_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_unfold_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_unfold_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_upsample_bilinear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_upsample_bilinear_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nn_functional_upsample_nearest_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_static_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_static_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_nonzero_static_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_norm_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_outer_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_outer_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_outer_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_outer_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_pca_lowrank_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_permute_copy_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_triu_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_triu_indices_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_triu_indices_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_true_divide_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_true_divide_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_uint16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unravel_index_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_complex_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_int32, test/test_meta.py::TestMetaCPU::test_quantized_embedding_bag_cpu, test/test_meta.py::TestMetaCPU::test_segment_reduce_backward_cpu
2025-03-14T05:54:27.3172760Z 
2025-03-14T05:54:31.2277340Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2025-03-14 05:54:31.227310]
2025-03-14T05:54:31.2278320Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:54:31.2280869Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_helper.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:54:31.227734]
2025-03-14T05:54:39.9070252Z 
2025-03-14T05:54:39.9073016Z torch_np/numpy_tests/fft/test_helper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_fd7ddb466c147c6f_.log
2025-03-14T05:54:39.9080187Z Running 8 items in this shard: test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_axes_keyword, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_equal_to_original, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_inverse, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_uneven_dims, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestRFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestIRFFTN::test_not_last_axis_success
2025-03-14T05:54:39.9085107Z 
2025-03-14T05:54:44.6464526Z Running test_type_promotion 1/1 ... [2025-03-14 05:54:44.646025]
2025-03-14T05:54:44.6465394Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:54:44.6468029Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_promotion.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:54:44.646441]
2025-03-14T05:54:52.4758272Z 
2025-03-14T05:54:52.4759295Z test_type_promotion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_promotion_1.1_efe1615fd0ffec2b_.log
2025-03-14T05:54:52.4930547Z Running 423 items in this shard: test/test_type_promotion.py::TestTypePromotionCPU::test_add_wrapped_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_alpha_mismatch_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_alternate_result_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_bfloat16_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_booleans_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_can_cast_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_cat_different_dtypes_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_cat_out_different_dtypes_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_comparison_ops_with_type_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_assertraises_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_half_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_scalar_mult_tensor_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_computation_ignores_out_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_create_bool_tensors_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_float_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_from_issue_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_half_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_indexing_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_indexing_fail_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_inplace_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_int_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_int_to_float_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_lt_with_type_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_many_promotions_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_mixed_type_backward_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_non_promoting_ops_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int8, 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test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_tensor_vs_scalar_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_add_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_mul_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_sub_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_ternary_out_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_transpose_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unsigned_cpu
2025-03-14T05:54:52.5095436Z 
2025-03-14T05:54:56.1167373Z Running test_linalg 1/1 ... [2025-03-14 05:54:56.115933]
2025-03-14T05:54:56.1168372Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:54:56.1170322Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:54:56.116301]
2025-03-14T05:58:25.9573052Z 
2025-03-14T05:58:25.9574303Z test_linalg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_1.1_d2aa640ebab35543_.log
2025-03-14T05:58:26.0415283Z Running 1226 items in this shard: test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float64, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_1_k_128_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_1_k_128_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_1_k_64_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_1_k_64_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_32_k_128_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_32_k_128_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_32_k_64_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_32_k_64_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_256_n_128_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_256_n_32_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_256_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_256_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_64_n_128_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_64_n_32_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_48_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_48_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_48_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_48_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_64_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_64_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_64_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_64_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_64_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_64_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_64_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_64_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_48_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_48_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_48_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_48_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_64_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_64_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_64_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_64_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_48_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_48_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_48_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_48_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_64_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_64_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_64_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_64_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, 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test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_32_use_transpose_a_True_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_baddbmm_overflow_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_gelu_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_gelu_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_gelu_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_0_0_cpu_float32, 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test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_1_0_cpu_float32, 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test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_1_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_1_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_1_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_1_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_1_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_tunableop_rocm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_tunableop_rocm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_tunableop_rocm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_sizes_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_sizes_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmv_rowmajor_colmajor_incx_incy_lda_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmv_rowmajor_colmajor_incx_incy_lda_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addr_bool_cpu_bool, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_addr_type_promotion_cpu, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_baddbmm_nan_input_with_zero_beta_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_blas_empty_cpu, test/test_linalg.py::TestLinalgCPU::test_blas_mv_large_input_cpu, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_bmm_tunableop_rocm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_broadcast_batched_matmul_cpu, test/test_linalg.py::TestLinalgCPU::test_broadcast_fused_matmul_cpu, test/test_linalg.py::TestLinalgCPU::test_chain_matmul_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_backward_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_ck_blas_library_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_1_k_128_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_1_k_128_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_1_k_64_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_1_k_64_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_32_k_128_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_32_k_128_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_32_k_64_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_32_k_64_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_cross_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cross_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cross_with_and_without_dim_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cross_with_and_without_dim_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_det_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_det_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_det_logdet_slogdet_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_det_logdet_slogdet_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_disable_tuning_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_dot_invalid_args_cpu, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_dump_results_on_exit_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_check_magma_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eig_numpy_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eig_numpy_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eig_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_eig_with_nan_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eig_with_nan_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eig_with_nan_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_with_nan_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_lwork_lapack_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_lwork_lapack_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_lwork_lapack_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigh_lwork_lapack_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_svd_illcondition_matrix_input_should_not_crash_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigh_svd_illcondition_matrix_input_should_not_crash_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigvals_numpy_cpu_complex128, 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2025-03-14T05:58:26.1234378Z 
2025-03-14T05:58:28.9663499Z 
2025-03-14T05:58:28.9664695Z test_ops 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_3.6_6ea169e9dc5ce68c_.log
2025-03-14T05:58:29.2837395Z Running 5777 items in this shard: test/test_ops.py::TestCommonCPU::test_compare_cpu___getitem___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___ror___cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_bfloat16_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_int_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_short_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_as_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_eye_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_fft_ifftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_hypot_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logit_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_minimum_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nanmean_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_narrow_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_ne_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_norm_nuc_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_permute_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_quantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_remainder_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sinc_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sinh_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_slice_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_bessel_y1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_entr_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_i1e_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_k0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_zeta_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_std_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_transpose_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_triangular_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_tril_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_unfold_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_var_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_view_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_vstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_T_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices___getitem___cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices___rmatmul___cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices__softmax_backward_data_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices__unsafe_masked_index_put_accumulate_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_abs_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addbmm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_all_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_all_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_allclose_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argmax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_argwhere_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bfloat16_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cdist_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_clone_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_combinations_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_constant_pad_nd_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_contiguous_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_copysign_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumprod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_diag_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diff_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_dist_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_einsum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_equal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exponential_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_eye_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fliplr_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_grid_sampler_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_half_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_histogramdd_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_hstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_igammac_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_mean_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_isclose_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isinf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_binary_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_kron_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_ldexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ldexp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cholesky_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cond_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_det_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_diagonal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eigh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_matrix_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_multi_dot_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_and_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logit_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logspace_tensor_overload_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_cumsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_logaddexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_scatter_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_std_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_matmul_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_meshgrid_variadic_tensors_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_minimum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mode_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mul_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nanmean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_neg_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_zeros_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_glu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softplus_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softsign_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nonzero_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_norm_inf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_randint_like_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_ravel_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_real_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_real_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_reciprocal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_remainder_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize__cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize__cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_rot90_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_rot90_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_rsqrt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_scalar_tensor_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_bessel_y0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_erfcx_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_hermite_polynomial_h_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i0e_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i1e_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_laguerre_polynomial_l_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_legendre_polynomial_p_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_log_ndtr_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_t_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_v_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_spherical_bessel_j0_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_zeta_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sqrt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_std_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_sub_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_t_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_take_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_tanh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_tile_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_transpose_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_trapz_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unfold_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsafe_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsafe_split_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsafe_split_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_var_mean_unbiased_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_vstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_zero__cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_H_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_T_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values___rand___cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values__chunk_cat_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_alias_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_as_strided_scatter_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_asin_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_atan_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_block_diag_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_broadcast_tensors_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cartesian_prod_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cdouble_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_column_stack_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cummax_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_deg2rad_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_diagonal_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_diagonal_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_exp2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_exp_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_ifft2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fill_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fliplr_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_float_power_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_full_like_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_half_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_i0_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_isinf_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_linalg_diagonal_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_log10_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_log_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_logical_and_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_masked_scatter_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_meshgrid_variadic_tensors_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_movedim_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_new_full_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_new_zeros_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_permute_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_put_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_rad2deg_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_roll_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_rsqrt_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_short_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_sin_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_legendre_polynomial_p_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_modified_bessel_k0_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_shifted_chebyshev_polynomial_v_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_shifted_chebyshev_polynomial_w_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_sum_to_size_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_take_along_dim_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_to_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_transpose_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_tril_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_unbind_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_unique_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_where_cpu_bool, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___getitem___cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rdiv___cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rmod___cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rsub___cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples__unsafe_masked_index_put_accumulate_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_acos_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_acosh_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_acosh_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_addmv_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_alias_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_all_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_argsort_cpu_int64, 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test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_avg_pool2d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_binary_cross_entropy_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv2d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv_transpose1d_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_gelu_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_local_response_norm_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_circular_cpu_complex64, 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test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_ormqr_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_polar_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_rsqrt_cpu_complex64, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_scatter_reduce_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_t_copy_cpu_complex64, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_t_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_tanh_cpu_complex64, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_tanh_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_unfold_copy_cpu_complex64, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_view_copy_cpu_complex64, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_view_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_vstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_where_cpu_complex64, test/test_ops.py::TestCommonCPU::test_out_requires_grad_error_where_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_resize_as__cpu_float32, test/test_ops.py::TestCommonCPU::test_out_roll_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_round_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_scatter_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_searchsorted_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_sigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_signal_windows_hamming_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_special_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_special_hermite_polynomial_he_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_special_ndtr_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_special_zeta_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_split_with_sizes_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_tensordot_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_trace_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_transpose_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_triu_indices_cpu_int64, test/test_ops.py::TestCommonCPU::test_out_unflatten_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_unfold_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_uniform_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_unsqueeze_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_var_cpu_float32, test/test_ops.py::TestCommonCPU::test_out_warning___rmod___cpu, test/test_ops.py::TestCommonCPU::test_out_warning__chunk_cat_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs__conversions_char_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs__conversions_complex_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_acosh_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_alias_copy_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_all_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_amax_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_amin_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_as_strided_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_bitwise_left_shift_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_bitwise_or_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_bitwise_right_shift_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_broadcast_shapes_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_bucketize_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_clamp_min_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_conj_physical_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_constant_pad_nd_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_copysign_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_div_trunc_rounding_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_exp_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_expand_as_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_expand_copy_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_exponential_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_fft_hfft2_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_fft_ifft_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_fmod_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_isneginf_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_istft_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_lerp_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_lgamma_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_log10_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_log1p_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_logical_not_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_logspace_tensor_overload_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_logsumexp_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_meshgrid_variadic_tensors_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_mul_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nan_to_num_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_narrow_copy_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_native_layer_norm_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_new_empty_strided_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_new_full_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_alpha_dropout_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_dropout_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_huber_loss_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_leaky_relu_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_log_softmax_with_dtype_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_relu6_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_softmax_with_dtype_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_softplus_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_nn_functional_threshold_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_prod_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_repeat_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_reshape_as_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_special_bessel_j1_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_special_log_ndtr_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_special_log_softmax_with_dtype_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_special_logit_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_special_ndtri_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_stack_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_std_mean_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_sum_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_unsqueeze_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_view_as_complex_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_vsplit_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__refs_xlogy_cpu, test/test_ops.py::TestCommonCPU::test_out_warning__softmax_backward_data_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_allclose_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_argmax_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_atan_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_atleast_3d_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_bitwise_left_shift_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_bitwise_right_shift_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_block_diag_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_bool_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_ceil_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_conj_physical_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_constant_pad_nd_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_contiguous_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_count_nonzero_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_cummax_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_cummin_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_einsum_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_empty_permuted_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_erfinv_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_exp2_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_expm1_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_fft_fft_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_fft_fftshift_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_fft_ifftshift_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_fft_ihfft2_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_fft_rfftn_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_fliplr_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_fmin_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_full_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_full_like_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_geometric_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_histogramdd_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_i0_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_index_copy_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_index_reduce_amax_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_isin_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_isneginf_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_isposinf_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_jiterator_4inputs_with_extra_args_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_le_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_cholesky_ex_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_eigh_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_inv_ex_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_ldl_factor_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_lstsq_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_lu_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_norm_subgradients_at_zero_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_qr_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_linalg_vecdot_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_log2_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_log_softmax_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_logical_and_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_logit_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_masked_norm_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_masked_sum_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_min_binary_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_narrow_copy_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_native_layer_norm_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_new_empty_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_new_full_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_avg_pool2d_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_batch_norm_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_binary_cross_entropy_with_logits_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_conv1d_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_conv_transpose1d_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_ctc_loss_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_leaky_relu_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_logsigmoid_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_max_unpool2d_grad_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_pad_reflect_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_pad_replicate_negative_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_pairwise_distance_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_pdist_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_pixel_unshuffle_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_relu6_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_silu_complex_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_smooth_l1_loss_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nn_functional_softsign_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_nonzero_static_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_outer_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_permute_copy_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_polygamma_polygamma_n_0_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_positive_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_quantile_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_rad2deg_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_renorm_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_resize_as__cpu, test/test_ops.py::TestCommonCPU::test_out_warning_rsqrt_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_scalar_tensor_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_scatter_reduce_amin_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_scatter_reduce_sum_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_select_scatter_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_sigmoid_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_sign_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_signal_windows_bartlett_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_slice_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_sort_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_special_chebyshev_polynomial_w_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_special_entr_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_special_modified_bessel_i0_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_special_modified_bessel_k0_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_special_polygamma_special_polygamma_n_0_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_special_scaled_modified_bessel_k1_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_special_shifted_chebyshev_polynomial_t_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_sqrt_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_square_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_squeeze_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_std_mean_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_sub_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_tanh_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_tensor_split_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_tile_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_trapezoid_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_triu_indices_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_unfold_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_view_as_cpu, test/test_ops.py::TestCommonCPU::test_out_warning_zeros_cpu, test/test_ops.py::TestCommonCPU::test_pointwise_tag_coverage_cpu, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_acos_cpu_int16, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_acosh_cpu_uint8, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_asin_cpu_int32, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_asin_cpu_int64, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_atan2_cpu_int32, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_atan2_cpu_int64, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_atanh_cpu_int32, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_copysign_cpu_int64, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_cos_cpu_int64, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_cos_cpu_int8, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_cosh_cpu_int8, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_div_no_rounding_mode_cpu_bool, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_div_no_rounding_mode_cpu_int8, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_erf_cpu_int32, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_erf_cpu_int64, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_erfc_cpu_int16, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_exp2_cpu_int8, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_exp_cpu_int32, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_exp_cpu_uint8, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_expm1_cpu_int16, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_i0_cpu_int8, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_ldexp_cpu_bool, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_ldexp_cpu_int16, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_ldexp_cpu_int64, test/test_ops.py::TestCommonCPU::test_promotes_int_to_float_log1p_cpu_int64, 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test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_new_zeros_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_glu_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_mish_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_prelu_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_relu6_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_norm_fro_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_pinverse_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_positive_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_qr_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_rad2deg_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_resize_as__cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_resolve_neg_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_bartlett_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_hamming_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_hann_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_slice_scatter_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_bessel_j1_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_bessel_y1_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_erfcx_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_xlog1py_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_split_list_args_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_sqrt_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_std_unbiased_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_sum_to_size_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_svd_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_svd_lowrank_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_tanh_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_trapz_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unflatten_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unique_consecutive_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unsqueeze_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_var_mean_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_view_as_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_view_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_zeros_like_cpu_float32, test/test_ops.py::TestMathBitsCPU::test_conj_view___rsub___cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_bfloat16_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_long_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_short_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_alias_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_atleast_3d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_count_nonzero_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_diagonal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_empty_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_empty_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_expand_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_fftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_fftshift_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_hfft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_ifftshift_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_irfft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_irfft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_irfftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_index_add_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_index_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_isfinite_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_isinf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_linalg_diagonal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_linalg_vecdot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_logical_xor_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_pow_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_prod_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_ravel_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_special_softmax_with_dtype_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_squeeze_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_squeeze_multiple_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_std_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_std_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_tensor_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_tril_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_view_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_vstack_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_alias_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_all_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_allclose_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_as_strided_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_block_diag_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_byte_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cholesky_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_diagflat_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_empty_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_equal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_exp2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_exp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_expand_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_expand_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_fftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_ifft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_flatten_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_float_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_geqrf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isclose_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isreal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_cholesky_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_eigvals_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_eigvalsh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_inv_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lu_factor_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_pinv_singular_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_slogdet_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_solve_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_svdvals_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_tensorsolve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_vector_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_log10_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_log2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_logspace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_select_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_sum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_meshgrid_variadic_tensors_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mul_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_narrow_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_narrow_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_new_empty_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_new_full_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv2d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv3d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_pixel_shuffle_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nonzero_static_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_ones_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_permute_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_qr_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_randn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_repeat_interleave_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_rsub_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_short_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sqrt_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tanh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_trace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_transpose_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_transpose_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_triangular_solve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unsqueeze_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unsqueeze_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_mean_unbiased_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_where_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_zeros_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___getitem___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__chunk_cat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_byte_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_short_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_acos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_acosh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_addcdiv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_addcmul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_all_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atleast_2d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cosh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_div_no_rounding_mode_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_dot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_expand_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_eye_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_hfft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_flatten_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_flip_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_index_select_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_svd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_svdvals_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_log2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logical_not_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_meshgrid_list_of_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_empty_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_ones_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_permute_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_reshape_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_roll_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_rsub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sinh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_square_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_squeeze_multiple_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_tanh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_tensor_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_transpose_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_true_divide_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unbind_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unsqueeze_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_acos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addmv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_all_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_partial_views_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_asin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_block_diag_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_broadcast_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_byte_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_chalf_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cholesky_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cov_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cross_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_div_no_rounding_mode_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dstack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_einsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_empty_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_expand_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_expand_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_flipud_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_gather_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_hsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_inner_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_int_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_binary_return_by_ref_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_kron_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_lerp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cond_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_diagonal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_inv_ex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lstsq_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lstsq_grad_oriented_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lu_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_slogdet_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_vander_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logspace_tensor_overload_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_lu_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_logsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_normalize_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_std_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_mul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv2d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv_transpose1d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_l1_loss_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_pad_replicate_negative_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_real_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_repeat_interleave_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_reshape_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sqrt_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_square_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_svd_lowrank_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tensor_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_trapz_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_vdot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_view_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_vsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view_T_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__chunk_cat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__native_batch_norm_legit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_char_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_as_strided_partial_views_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_asinh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atan2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atleast_2d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_block_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cauchy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_constant_pad_nd_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cumprod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diagonal_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_dsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_eq_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_fft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ihfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_rfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_frac_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_index_select_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_svdvals_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_vecdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logaddexp2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_meshgrid_list_of_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_native_layer_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_neg_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_empty_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_ones_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_celu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_pixel_shuffle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_prelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_round_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_signbit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sinc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_bessel_j0_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_i1e_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_log_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_logit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_multigammaln_mvlgamma_p_5_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_ndtr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_squeeze_multiple_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_stack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_stft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_tan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_true_divide_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_view_as_complex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_vsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__unsafe_masked_index_put_accumulate_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_block_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bucketize_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cauchy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ceil_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_chalf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cholesky_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_complex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_count_nonzero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cummin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cumsum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diagonal_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_empty_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_expm1_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_fft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_fftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_hfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_hfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_ifft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_ifftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_flip_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_full_like_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_gather_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_geometric_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_histc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_index_fill_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_inner_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_isposinf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_isreal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_le_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_det_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_diagonal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_eig_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_lu_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_matrix_power_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_matrix_rank_hermitian_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_norm_subgradients_at_zero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_vecdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_logspace_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_lu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_amax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_cumprod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_log_softmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_prod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_softmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_var_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_max_reduction_no_dim_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_meshgrid_list_of_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_minimum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_msort_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_mv_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_narrow_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_dropout3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_fractional_max_pool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_gaussian_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_instance_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_interpolate_linear_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_interpolate_nearest_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_local_response_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_logsigmoid_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_pool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool3d_grad_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pad_reflect_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pad_replicate_negative_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pixel_shuffle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_rrelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_selu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_normal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ones_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ormqr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_outer_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_randint_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_randn_like_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_repeat_interleave_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_resolve_neg_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_scalar_tensor_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_signal_windows_bartlett_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_signal_windows_cosine_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_signal_windows_general_cosine_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sinh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_slice_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sparse_sampled_addmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_special_bessel_j0_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_special_legendre_polynomial_p_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_split_with_sizes_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sqrt_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_tan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_tanh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_triangular_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_unfold_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_unsafe_chunk_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_unsafe_split_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_var_mean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_vdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_view_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_xlogy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_zero__cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_zeros_like_cpu_float64, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_T_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_bfloat16_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_bool_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_chalf_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_double_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_long_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_acosh__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_add__cpu, 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test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_relu6_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_rrelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nonzero_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_normal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_randint_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_randn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_resolve_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_short_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_bessel_y0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_entr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_xlog1py_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_split_list_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_tensordot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_tile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_view_as_real_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_bincount_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_bitwise_or_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_bmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cdouble_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ceil_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_clone_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_column_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_addmm_decomposed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_as_strided_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_asin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_atleast_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_block_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_broadcast_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_clamp_max_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_clamp_min_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_clone_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cov_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_einsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_expand_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_floor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_frexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_gradient_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_kron_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_kthvalue_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_qr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_vander_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_vecdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_log_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_logaddexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_max_binary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_native_dropout_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_fro_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_ndtri_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_square_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_tril_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unbind_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unfold_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unsafe_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_var_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rdiv___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rmod___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__upsample_bilinear2d_aa_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addmv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_partial_views_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_asinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atleast_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atleast_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_clamp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_clamp_min_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_copysign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diag_embed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagonal_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_expand_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_frexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_index_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_index_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cond_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_svdvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hardsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_silu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_upsample_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_permute_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_permute_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_round_decimals_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sinc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_slice_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_squeeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unflatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_var_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_empty_permuted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_eq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_erfinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_exp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_fft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_float_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_full_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gcd_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_geometric_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_geqrf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gradient_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_imag_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_index_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_isinf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_2inputs_2outputs_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_unary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_le_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_eig_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_eigvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_inv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_matrix_rank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_slogdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log10_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logical_and_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logical_xor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_msort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_multinomial_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nan_to_num_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_narrow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_gelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_mish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_prelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_rrelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_selu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softsign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_fro_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_normal_in_place_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ones_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_pca_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_positive_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_randn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_resize__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rsqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_kaiser_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signbit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_slice_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_bessel_j1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_std_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_to_sparse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_topk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tril_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_triu_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_unbind_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unflatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_as_real_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_view_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_where_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_zero__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_T_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___getitem___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rmatmul___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__unsafe_masked_index_put_accumulate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addmv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_angle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_any_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_as_strided_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atleast_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bernoulli_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bfloat16_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bincount_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_char_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_clamp_max_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_digamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_div_trunc_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_erf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expm1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_fftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ihfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_flipud_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_floor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fmod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_gt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_igamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_item_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lerp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lgamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_solve_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logaddexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_argmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nansum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_narrow_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_native_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_permute_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_renorm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scalar_tensor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_bessel_y0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_log_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_ndtri_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_true_divide_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unsafe_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unsqueeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_complex32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags___rsub___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__batch_norm_with_update_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_bool_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_chalf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_float_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_int_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_polar_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_addcmul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_amax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_broadcast_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_clamp_min_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_constant_pad_nd_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_copysign_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cos_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cosh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_dsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_expand_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_fftshift_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_hfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ifftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_floor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_hsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_isnan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_log10_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_masked_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_minimum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_narrow_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_ne_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_neg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_elu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_softplus_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_pow_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rad2deg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_remainder_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_renorm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_repeat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_reshape_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rsub_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sigmoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_signbit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_i1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_log_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_split_with_sizes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_square_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_squeeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_squeeze_multiple_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_std_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_triu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_triu_indices_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_unfold_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_unsqueeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_vdot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__segment_reduce_lengths_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__unsafe_masked_index_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_abs_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_any_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_as_strided_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bitwise_xor_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_broadcast_shapes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cauchy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_clamp_min_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_clone_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_column_stack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_conj_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_count_nonzero_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cummax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diagonal_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_permuted_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_erfinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_exp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_expand_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_eye_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_fftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_hfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_irfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_rfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fliplr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_float_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_floor_divide_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_full_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_geometric_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_gradient_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_histogram_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_i0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_igammac_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isclose_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ldexp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_lerp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cholesky_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cond_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cross_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_det_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_ldl_factor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_slogdet_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_svdvals_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_tensorinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_tensorsolve_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log10_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log1p_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_normal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mH_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_argmin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_cumsum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_median_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_sum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_minimum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_gelu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_mse_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_norm_nuc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_normal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ones_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ones_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_pca_lowrank_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_pinverse_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_polar_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rand_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_real_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reshape_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reshape_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_resolve_neg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_blackman_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_cosine_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_hann_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sinh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_bessel_y1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_log_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_zeta_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sqrt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_squeeze_multiple_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_stack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_std_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sub_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tensor_split_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_trace_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_transpose_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_true_divide_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unbind_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsqueeze_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsqueeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_view_as_complex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_view_as_real_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags_view_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_zeros_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_zeros_like_cpu_float32
2025-03-14T05:58:29.4951692Z 
2025-03-14T05:58:30.3229957Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2025-03-14 05:58:30.322578]
2025-03-14T05:58:30.3230942Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:58:30.3233187Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:58:30.322924]
2025-03-14T05:58:33.5720199Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2025-03-14 05:58:33.571643]
2025-03-14T05:58:33.5720845Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:58:33.5722211Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_methods.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:58:33.571973]
2025-03-14T05:58:35.3454835Z 
2025-03-14T05:58:35.3456613Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_d3f867fa43e43bb7_.log
2025-03-14T05:58:35.3504513Z Running 62 items in this shard: test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary0_prepend0_append_nan_expected_to_end, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary1_prepend1_append1_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary2_prepend_nan_append_nan_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary0_prepend_65536_append_65540_expected0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary1_prepend1_append1_expected1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary2_prepend_0_append_0_expected2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary3_prepend_3_append_-9_expected3, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_are_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_have_structured_dtype, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_first_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_hit_alternate_algorithm, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_second_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_table_timedelta_fails, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_with_arrays_containing_tuples, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_array_like, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_indices, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_manyways, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_unique, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setxor1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_union1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_-1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_list, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_zeros, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_nanequals, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_sort_order_with_axis
2025-03-14T05:58:35.3551079Z 
2025-03-14T05:58:37.6925635Z 
2025-03-14T05:58:37.6927594Z torch_np/numpy_tests/core/test_scalar_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_469728def04b4517_.log
2025-03-14T05:58:37.6960990Z Running 80 items in this shard: test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_against_known_values, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype0_frac_vals0_exp_vals0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype1_frac_vals1_exp_vals1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype2_frac_vals2_exp_vals2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_non_numeric_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_?, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_D, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_F, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_scalar, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetitemMisc::test_class_getitem_38_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetitemMisc::test_class_getitem_38_cls1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetitemMisc::test_class_getitem_38_cls2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_bit_count, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype6, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype7
2025-03-14T05:58:37.6993222Z 
2025-03-14T05:58:40.0313961Z Running test_sparse 1/1 ... [2025-03-14 05:58:40.031025]
2025-03-14T05:58:40.0314712Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:58:40.0317107Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sparse.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:58:40.031346]
2025-03-14T05:58:42.1142053Z Running functorch/test_aotdispatch 4/5 ... [2025-03-14 05:58:42.113794]
2025-03-14T05:58:42.1142992Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T05:58:42.1145103Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'not serial', '--shard-id=4', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 05:58:42.114145]
2025-03-14T06:03:48.6735646Z 
2025-03-14T06:03:48.6737154Z functorch/test_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_1.1_6bb4d4bd89b3e8f2_.log
2025-03-14T06:03:48.8609102Z Running 2120 items in this shard: test/functorch/test_vmap.py::TestVmapAPI::test_accepts_nested_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_backward_unsupported_interaction, test/functorch/test_vmap.py::TestVmapAPI::test_batch_rule_does_not_need_to_handle_no_batched_input, test/functorch/test_vmap.py::TestVmapAPI::test_batched_gradient_basic, test/functorch/test_vmap.py::TestVmapAPI::test_checkpoint, test/functorch/test_vmap.py::TestVmapAPI::test_constant_function, test/functorch/test_vmap.py::TestVmapAPI::test_data_attribute, test/functorch/test_vmap.py::TestVmapAPI::test_data_dependent_control_flow_throws, test/functorch/test_vmap.py::TestVmapAPI::test_decomposition_under_python_dispatcher, test/functorch/test_vmap.py::TestVmapAPI::test_different_map_dim_size_raises, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_does_not_warn_by_default, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_masked_fill, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_multiple_returns, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warning, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warns_when_warnings_are_enabled, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_with_undefined_grad, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_zero_dim, test/functorch/test_vmap.py::TestVmapAPI::test_func_with_no_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_func_with_no_tensors, test/functorch/test_vmap.py::TestVmapAPI::test_functools_partial, test/functorch/test_vmap.py::TestVmapAPI::test_grad_unsupported_interaction, test/functorch/test_vmap.py::TestVmapAPI::test_in_dim_not_in_tensor_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_in_dims_wrong_type_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_different_levels, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_same_levels, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_unary, test/functorch/test_vmap.py::TestVmapAPI::test_integer_in_dim_but_not_tensor_input_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_item_throws, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs2, test/functorch/test_vmap.py::TestVmapAPI::test_nested_negative_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_non_default_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_diag_embed, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_different_map_dim, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_same_map_dim, test/functorch/test_vmap.py::TestVmapAPI::test_nn_module, test/functorch/test_vmap.py::TestVmapAPI::test_non_default_in_dims_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_non_tensor_output_raises, test/functorch/test_vmap.py::TestVmapAPI::test_non_zero_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_none_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nonzero_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_noop_in_inner_vmap, test/functorch/test_vmap.py::TestVmapAPI::test_not_enough_in_dims_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dim_out_of_bounds_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_and_num_outputs_mismatch_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_edge_case, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_must_be_int_or_collection_of_int_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_none, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_none_tuple, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_normal_tensor, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_odict_returns, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_broadcast_nested, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_broadcast_simple, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_outdims, test/functorch/test_vmap.py::TestVmapAPI::test_reshape_dim_into, test/functorch/test_vmap.py::TestVmapAPI::test_reshape_dim_outof, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_no_vmapped_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_pytree_input_output, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_unexpanded_outputs, test/functorch/test_vmap.py::TestVmapAPI::test_single_input, test/functorch/test_vmap.py::TestVmapAPI::test_unsupported_op_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cpu, test/functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cuda, test/functorch/test_vmap.py::TestVmapOperators::test_T_numpy, test/functorch/test_vmap.py::TestVmapOperators::test_adaptive_avg_pool2d, test/functorch/test_vmap.py::TestVmapOperators::test_argmax_dim, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_div, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_div_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_as_strided, test/functorch/test_vmap.py::TestVmapOperators::test_bmm, test/functorch/test_vmap.py::TestVmapOperators::test_cat, test/functorch/test_vmap.py::TestVmapOperators::test_chunk, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_clamp, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_max_, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_min_, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_max, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_min, test/functorch/test_vmap.py::TestVmapOperators::test_clone, test/functorch/test_vmap.py::TestVmapOperators::test_comparison_ops, test/functorch/test_vmap.py::TestVmapOperators::test_conj, test/functorch/test_vmap.py::TestVmapOperators::test_conj_bit, test/functorch/test_vmap.py::TestVmapOperators::test_contiguous, test/functorch/test_vmap.py::TestVmapOperators::test_conv2d, test/functorch/test_vmap.py::TestVmapOperators::test_copy_, test/functorch/test_vmap.py::TestVmapOperators::test_cross_batch_size_three, test/functorch/test_vmap.py::TestVmapOperators::test_diagonal, test/functorch/test_vmap.py::TestVmapOperators::test_dot, test/functorch/test_vmap.py::TestVmapOperators::test_expand_as, test/functorch/test_vmap.py::TestVmapOperators::test_fill_and_zero_inplace, test/functorch/test_vmap.py::TestVmapOperators::test_imag, test/functorch/test_vmap.py::TestVmapOperators::test_is_complex, test/functorch/test_vmap.py::TestVmapOperators::test_is_contiguous, test/functorch/test_vmap.py::TestVmapOperators::test_is_floating_point, test/functorch/test_vmap.py::TestVmapOperators::test_mean, test/functorch/test_vmap.py::TestVmapOperators::test_mean_dim, test/functorch/test_vmap.py::TestVmapOperators::test_mm, test/functorch/test_vmap.py::TestVmapOperators::test_mode_key, test/functorch/test_vmap.py::TestVmapOperators::test_movedim, test/functorch/test_vmap.py::TestVmapOperators::test_mv, test/functorch/test_vmap.py::TestVmapOperators::test_narrow, test/functorch/test_vmap.py::TestVmapOperators::test_new_empty, test/functorch/test_vmap.py::TestVmapOperators::test_new_empty_strided, test/functorch/test_vmap.py::TestVmapOperators::test_new_zeros, test/functorch/test_vmap.py::TestVmapOperators::test_nll_loss, test/functorch/test_vmap.py::TestVmapOperators::test_one_hot, test/functorch/test_vmap.py::TestVmapOperators::test_real, test/functorch/test_vmap.py::TestVmapOperators::test_repeat, test/functorch/test_vmap.py::TestVmapOperators::test_reshape, test/functorch/test_vmap.py::TestVmapOperators::test_reshape_as, test/functorch/test_vmap.py::TestVmapOperators::test_result_type, test/functorch/test_vmap.py::TestVmapOperators::test_roll_no_dims, test/functorch/test_vmap.py::TestVmapOperators::test_select, test/functorch/test_vmap.py::TestVmapOperators::test_silu_backward, test/functorch/test_vmap.py::TestVmapOperators::test_slice, test/functorch/test_vmap.py::TestVmapOperators::test_slogdet, test/functorch/test_vmap.py::TestVmapOperators::test_split, test/functorch/test_vmap.py::TestVmapOperators::test_squeeze, test/functorch/test_vmap.py::TestVmapOperators::test_stack, test/functorch/test_vmap.py::TestVmapOperators::test_stride, test/functorch/test_vmap.py::TestVmapOperators::test_sum, test/functorch/test_vmap.py::TestVmapOperators::test_sum_dim, test/functorch/test_vmap.py::TestVmapOperators::test_t, test/functorch/test_vmap.py::TestVmapOperators::test_tensor_split, test/functorch/test_vmap.py::TestVmapOperators::test_to, test/functorch/test_vmap.py::TestVmapOperators::test_trace, test/functorch/test_vmap.py::TestVmapOperators::test_transpose, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_abs, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_acos, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_asin, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_atan, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_ceil, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cos, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cosh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_digamma, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_exp, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_expm1, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_floor, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_frac, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_lgamma, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log10, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log1p, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log2, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_neg, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_reciprocal, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_relu, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_round, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_rsqrt, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sigmoid, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sign, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sin, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sinh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sqrt, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_tan, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_tanh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_trunc, test/functorch/test_vmap.py::TestVmapOperators::test_unbind, test/functorch/test_vmap.py::TestVmapOperators::test_unfold, test/functorch/test_vmap.py::TestVmapOperators::test_unsafe_view, test/functorch/test_vmap.py::TestVmapOperators::test_unsqueeze, test/functorch/test_vmap.py::TestVmapOperators::test_view, test/functorch/test_vmap.py::TestVmapOperators::test_view_as, test/functorch/test_vmap.py::TestVmapOperators::test_view_as_complex, test/functorch/test_vmap.py::TestVmapOperators::test_view_as_real, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestVmapOperators::test_weird_matmul_case, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_0d_tensor_index_put_inplace_False_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_0d_tensor_index_put_inplace_True_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_advanced_indexing_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_False_track_running_stats_False_affine_False_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_False_track_running_stats_False_affine_True_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_False_track_running_stats_True_affine_False_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_False_track_running_stats_True_affine_True_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_True_track_running_stats_False_affine_False_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_True_track_running_stats_False_affine_True_cpu, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_pinv_hermitian_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_pinv_singular_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_qr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_slogdet_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_solve_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_solve_ex_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_solve_triangular_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_svd_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_svdvals_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_tensorinv_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_tensorsolve_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_vander_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_vecdot_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_vector_norm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_multi_dot_failure_1D_input_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_with_anomaly_detection_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_add_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_binary_cross_entropy_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_diagonal_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_div_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_expand_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_index_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_inplace_manyview_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_inplace_view_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_lgamma_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_log1p_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_log_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_log_softmax_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_logsumexp_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_max_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_median_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_min_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_mul_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_permute_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_randomness_backend0_randomness_different_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_randomness_backend0_randomness_error_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_randomness_backend0_randomness_same_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_reshape_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_sdpa_backend0_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_select_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_sigmoid_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_slice_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_stack_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_sub_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_threshold_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_trace_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_unrelated_output_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_unrelated_output_multiple_grad_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_where_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_grad_and_value_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_grad_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_jacfwd_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_jacrev_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_jvp_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_vjp_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_vmap_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_0_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_0_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_0_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_1_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_1_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_1_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_2_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_2_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_2_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_unbatched_randomness_different_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_unbatched_randomness_error_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_unbatched_randomness_same_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_different_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_different_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_error_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_error_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_same_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_same_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_first_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_first_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_last_dim_2_cpu, 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test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_different_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_different_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_error_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_error_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_same_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_same_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_unsupported_random_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_0_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_0_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_0_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_2_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_2_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_2_out_dim_2_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test__is_all_true_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test__is_any_true_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test_check_tensor_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_cat_batching_rule_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_binary_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_binary_nt_and_batched_dense_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_binary_nt_and_unbatched_dense_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_unary_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_with_nt_and_batched_dense_with_nonzero_bdim_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_multilevel_vmap_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_nt_acts_as_dense_in_vmap_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_nt_with_nonzero_in_dim_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_nt_with_nonzero_out_dim_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_shape_call_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_vmap_fallback_check_ok
2025-03-14T06:03:49.0442382Z 
2025-03-14T06:03:54.2391667Z Running test_quantization 3/3 ... [2025-03-14 06:03:54.238630]
2025-03-14T06:03:54.2392447Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:03:54.2394912Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-14 06:03:54.239127]
2025-03-14T06:08:06.0319999Z 
2025-03-14T06:08:06.0320895Z test_sparse 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_1.1_52258502d41ba858_.log
2025-03-14T06:08:06.1392561Z Running 3054 items in this shard: test/test_sparse.py::TestSparseLegacyAndDeprecation::test_legacy_warnings, test/test_sparse.py::TestSparseOneOff::test_cuda_from_cpu, test/test_sparse.py::TestSparseOneOff::test_cuda_sparse_cpu_dense_add, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_fake_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_fake_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_fake_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_fake_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_fake_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_meta_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_print_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_print_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_print_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_print_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_print_meta_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_to_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_to_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_to_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_to_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_to_meta_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_fake_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_fake_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_fake_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_fake_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_fake_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseCSR_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_expm1_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_floor_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_floor_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_floor_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_floor_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_floor_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_floor_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_floor_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_frac_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_frac_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_isinf_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_isinf_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_isinf_cpu_float32, 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test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_amin_cpu_int8, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_amin_cpu_uint8, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_bfloat16, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_bool, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_complex128, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_complex64, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_float16, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_float32, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_float64, test/test_sparse.py::TestSparseMaskedReductionsCPU::test_future_empty_dim_masked_prod_cpu_int16, 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test/test_sparse.py::TestSparseCPU::test_index_select_exhaustive_index_large_cpu_float64, test/test_sparse.py::TestSparseCPU::test_index_select_exhaustive_index_small_cpu_complex128, test/test_sparse.py::TestSparseCPU::test_index_select_exhaustive_index_small_cpu_float64, test/test_sparse.py::TestSparseCPU::test_index_select_parallelization_cpu_complex128, test/test_sparse.py::TestSparseCPU::test_index_select_parallelization_cpu_float64, test/test_sparse.py::TestSparseCPU::test_is_nonzero_cpu, test/test_sparse.py::TestSparseCPU::test_is_sparse_cpu, test/test_sparse.py::TestSparseCPU::test_isnan_cpu, test/test_sparse.py::TestSparseCPU::test_legacy_new_cpu, test/test_sparse.py::TestSparseCPU::test_legacy_new_device_cpu, test/test_sparse.py::TestSparseCPU::test_log1p_cpu_float32, test/test_sparse.py::TestSparseCPU::test_log1p_cpu_float64, test/test_sparse.py::TestSparseCPU::test_log1p_cpu_int16, test/test_sparse.py::TestSparseCPU::test_log1p_cpu_int32, 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2025-03-14T06:08:06.2452319Z 
2025-03-14T06:08:46.0533593Z 
2025-03-14T06:08:46.0534763Z functorch/test_aotdispatch 4/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_4.5_4ef6fb6819814857_.log
2025-03-14T06:08:46.0759731Z Running 435 items in this shard: test/functorch/test_aotdispatch.py::TestAOTAutograd::test_alias_of_intermediate_detach_backend_aot_eager_view_replay_for_aliased_outputs_False_dynamic_shapes_False, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_autocast_disable_guard, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_batchnorm_inference, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_buffer_copied_in_graph_with_different_shapes, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_default_partitioner_saves_symints_not_tensors_for_bw, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_dynamic_output_aliases_input_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_grad_context, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_inference_mode, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_aliases_and_none_require_gradients, 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test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_alias_of_intermediate_detach_backend_aot_eager_view_replay_for_aliased_outputs_False_dynamic_shapes_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_alias_of_intermediate_detach_backend_inductor_view_replay_for_aliased_outputs_False_dynamic_shapes_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_backward_mutation_forward_inputs, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_backward_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_complex_linear, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_custom_tensor_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_dupe_arg, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_inference_mode, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_data_and_metadata_mutation, 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test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_output_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_real_weights_in_symbolic_mode, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_set__and_data_mutation_good, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_alias_of_intermediate_detach_backend_aot_eager_view_replay_for_aliased_outputs_False_dynamic_shapes_True, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_mutation_forward_inputs_create_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_mutation_on_grad_out, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_batch_norm_amp, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_composite_impl_compile, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_custom_autograd, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_embedding_bag_view_dynamic, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_grad_context, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_alias_everything, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_hidden_from_autograd_aliasing, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_is_output, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_metadata2, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_requires_grad_detach, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_requires_grad_no_grad_inference_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_simple_with_none_and_nontensor, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_storage_resize_down_and_set_, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_output_view_simple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_invalid_requires_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nested_subclasses, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nested_subclasses_complicated_inps_mixed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_non_tensor_and_none_inputs, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_input_multi_output_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_input_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_and_returned, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_all_alias_types
2025-03-14T06:08:46.0980419Z 
2025-03-14T06:14:36.6130094Z 
2025-03-14T06:14:36.6131076Z test_quantization 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.3_8d18938052d617df_.log
2025-03-14T06:14:36.6268909Z Running 412 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool, test/test_quantization.py::TestQuantizedOps::test_advanced_indexing, test/test_quantization.py::TestQuantizedOps::test_batch_norm, test/test_quantization.py::TestQuantizedOps::test_batch_norm_relu, test/test_quantization.py::TestQuantizedOps::test_equal, test/test_quantization.py::TestQuantizedOps::test_interpolate, test/test_quantization.py::TestQuantizedOps::test_interpolate3d, test/test_quantization.py::TestQuantizedOps::test_linear_bias_unpack, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_cudnn, test/test_quantization.py::TestQuantizedOps::test_max_pool3d, test/test_quantization.py::TestQuantizedOps::test_max_pool3d_nhwc, test/test_quantization.py::TestQuantizedOps::test_qadd_broadcast, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_different_qparams, test/test_quantization.py::TestQuantizedOps::test_qclamp, test/test_quantization.py::TestQuantizedOps::test_qgelu, test/test_quantization.py::TestQuantizedOps::test_qsoftmax, test/test_quantization.py::TestQuantizedOps::test_qtanh, test/test_quantization.py::TestQNNPackOps::test_adaptive_avg_pool2d, test/test_quantization.py::TestQNNPackOps::test_hardtanh, test/test_quantization.py::TestQNNPackOps::test_qnnpack_relu, test/test_quantization.py::TestQuantizedLinear::test_qlinear, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_leaky_relu, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_pt2e, test/test_quantization.py::TestQuantizedLinear::test_wrapped_quantized_linear_prepacked, test/test_quantization.py::TestQuantizedConv::test_benchmark, test/test_quantization.py::TestQuantizedConv::test_conv_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_qconv1d, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_float_output_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_unpack, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose1d, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose3d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv2d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose2d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose3d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_dynamic_fp16, test/test_quantization.py::TestDynamicQuantizedOps::test_qrnncell, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_2d_indices, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_2bit, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_4bit, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_byte, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quantize_per_channel_affine_scale_dtypes, test/test_quantization.py::TestFakeQuantizeOps::test_forward_backward_per_tensor_with_amp, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_half_precision_numerics, test/test_quantization.py::TestFakeQuantizeOps::test_fq_module_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_channel_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_channel_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_tensor_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_tensor_cuda, test/test_quantization.py::TestQuantizedTensor::test_bfp16_quantize, test/test_quantization.py::TestQuantizedTensor::test_choose_qparams, test/test_quantization.py::TestQuantizedTensor::test_clone, test/test_quantization.py::TestQuantizedTensor::test_compare_per_tensor_device_numerics, test/test_quantization.py::TestQuantizedTensor::test_cuda_quantization_does_not_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dynamic_quant_pattern, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel_group, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_tensor_bfloat16_input, test/test_quantization.py::TestQuantizedTensor::test_qtensor_channel_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel_nhwc, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_load_save, test/test_quantization.py::TestQuantizedTensor::test_qtensor_per_channel_permute, test/test_quantization.py::TestQuantizedTensor::test_qtensor_resize, test/test_quantization.py::TestQuantizedTensor::test_quant_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_repeat, test/test_quantization.py::TestQuantizedTensor::test_torch_qtensor_deepcopy, test/test_quantization.py::TestFakeQuantize::test_quant_min_max_override, test/test_quantization.py::TestObserver::test_histogram_observer_consistent_buffer_shape, test/test_quantization.py::TestObserver::test_histogram_observer_handle_OOM_due_to_close_min_max_value, test/test_quantization.py::TestObserver::test_histogram_observer_save_load_state_dict, test/test_quantization.py::TestObserver::test_per_channel_observers_load_state_dict, test/test_quantization.py::TestObserver::test_state_dict_respects_device_affinity, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm2d, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add, test/test_quantization.py::TestStaticQuantizedModule::test_conv3d_api, test/test_quantization.py::TestStaticQuantizedModule::test_embedding_api, test/test_quantization.py::TestStaticQuantizedModule::test_leaky_relu, test/test_quantization.py::TestStaticQuantizedModule::test_linear, test/test_quantization.py::TestStaticQuantizedModule::test_linear_tanh, test/test_quantization.py::TestStaticQuantizedModule::test_prelu, test/test_quantization.py::TestStaticQuantizedModule::test_relu, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv1d, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv3d, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_convtranspose3d, test/test_quantization.py::TestReferenceQuantizedModule::test_linear_decomposed_weight_custom_qmin_qmax, test/test_quantization.py::TestReferenceQuantizedModule::test_rnn_cell, test/test_quantization.py::TestRecordHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestHistogramObserver::test_histogram_observer, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_against_reference, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_correct_numel, test/test_quantization.py::TestDistributed::test_fake_quant_preserves_buffers, test/test_quantization.py::TestDistributed::test_observers_preserve_buffers, test/test_quantization.py::TestBackendConfig::test_backend_config_set_backend_pattern_config, test/test_quantization.py::TestBackendConfig::test_backend_config_set_name, test/test_quantization.py::TestBackendConfig::test_backend_op_config_from_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_root_node_getter, test/test_quantization.py::TestBackendConfig::test_backend_op_config_to_dict, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_complex_args, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_simple, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_channel, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_tensor, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptdq, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptsq, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_dequant_stub, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_manual, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_mha_batch_first_attr_is_copied_in_prepare, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested2, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested3, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_skip_quant, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_two_layers, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_linear_relu_fusion, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_nested2, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_per_channel_linear_quantize, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_quantized_rnn_cell, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_two_layers, test/test_quantization.py::TestQuantizeEagerOps::test_conv_1d, test/test_quantization.py::TestQuantizeEagerOps::test_conv_3d, test/test_quantization.py::TestQuantizeEagerOps::test_leaky_relu, test/test_quantization.py::TestQuantizeEagerOps::test_relu, test/test_quantization.py::TestQuantizeEagerQAT::test_dropout, test/test_quantization.py::TestQuantizeEagerQAT::test_dynamic_qat_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_embedding_qat_qconfig_equal, test/test_quantization.py::TestQuantizeEagerQAT::test_eval_only_fake_quant, test/test_quantization.py::TestQuantizeEagerQAT::test_manual, test/test_quantization.py::TestQuantizeEagerQAT::test_train_save_load_eval, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_relu, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_fixed_qparam_ops, test/test_quantization.py::TestFuseEager::test_fuse_function_customization, test/test_quantization.py::TestFuseEager::test_fuse_module_eval, test/test_quantization.py::TestFuseEager::test_fuse_modules_with_nested_hooks, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_eval, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_train, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_functional_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_mobilenet_v2, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_convrelu, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_linearrelu, test/test_quantization.py::TestFuseFx::test_fuse_addtional_fuser_method, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_lowering, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_eval, test/test_quantization.py::TestFuseFx::test_fuse_linear_tanh_for_onednn_backend, test/test_quantization.py::TestFuseFx::test_fuse_module_relu, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_matchallnode, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_multiple_inputs, test/test_quantization.py::TestFuseFx::test_problematic_fuse_example, test/test_quantization.py::TestFuseFx::test_qconfig_fused_module, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx, test/test_quantization.py::TestQuantizeFx::test_conv_linear_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_lowering, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_set_observed_to_quantized_mapping, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_duplicate_nodes, test/test_quantization.py::TestQuantizeFx::test_default_qconfig_mapping_override_global, test/test_quantization.py::TestQuantizeFx::test_dynamic_linear_input_multiple_use, test/test_quantization.py::TestQuantizeFx::test_get_executorch_backend_config, test/test_quantization.py::TestQuantizeFx::test_linear_qint8_activation, test/test_quantization.py::TestQuantizeFx::test_linear_shape_view, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_conv_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_match_pattern_with_multiple_args, test/test_quantization.py::TestQuantizeFx::test_not_used, test/test_quantization.py::TestQuantizeFx::test_packed_weight_fused_op, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_input_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_names, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_output_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_preserve_tuple, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_tuple_args, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_list_args, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qat_and_script, test/test_quantization.py::TestQuantizeFx::test_qat_skip_untraced, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_setup, test/test_quantization.py::TestQuantizeFx::test_qconfig_function, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_repr, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_global, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_to_dict, test/test_quantization.py::TestQuantizeFx::test_qconfig_none, test/test_quantization.py::TestQuantizeFx::test_qconfig_precedence, test/test_quantization.py::TestQuantizeFx::test_qparams_buffers, test/test_quantization.py::TestQuantizeFx::test_quant_output_always_observed, test/test_quantization.py::TestQuantizeFx::test_quantized_model_type, test/test_quantization.py::TestQuantizeFx::test_ref_linear_module, test/test_quantization.py::TestQuantizeFx::test_save_observer_state_dict, test/test_quantization.py::TestQuantizeFx::test_size_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_standalone_module_float_interface, test/test_quantization.py::TestQuantizeFx::test_static_lstm_consume_tuple, test/test_quantization.py::TestQuantizeFx::test_static_lstm_with_custom_fixed_qparams, test/test_quantization.py::TestQuantizeFx::test_torch_unsqueeze_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_trace_quantize_per_tensor, test/test_quantization.py::TestQuantizeFx::test_unsqueeze__nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_view_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_add_relu, test/test_quantization.py::TestQuantizeFxOps::test_add_relu_multiple_uses_of_relu, test/test_quantization.py::TestQuantizeFxOps::test_bmm, test/test_quantization.py::TestQuantizeFxOps::test_clamp, test/test_quantization.py::TestQuantizeFxOps::test_conv_transpose_2d, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_wrong_qconfig, test/test_quantization.py::TestQuantizeFxOps::test_functional_conv, test/test_quantization.py::TestQuantizeFxOps::test_functional_linear, test/test_quantization.py::TestQuantizeFxOps::test_getitem, test/test_quantization.py::TestQuantizeFxOps::test_narrow, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle, test/test_quantization.py::TestQuantizeFxOps::test_ref_pattern_multi_use, test/test_quantization.py::TestQuantizeFxOps::test_rnn, test/test_quantization.py::TestQuantizeFxOps::test_softmax_reference, test/test_quantization.py::TestQuantizeFxModels::test_model_dropout, test/test_quantization.py::TestQuantizeFxModels::test_switch_device_prepare_convert, test/test_quantization.py::TestQuantizeFxModels::test_torchvision, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_quantization.py::TestGraphUtils::test_customized_equivalet_types_dict, test/test_quantization.py::TestDuplicateDQPass::test_no_add_quant_duplicate_dq, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_dq_no_static_q, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_two_dq, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting_through_unknown_ops, test/test_quantization.py::TestMetaDataPorting::test_simple_metadata_porting, test/test_quantization.py::TestNumericDebugger::test_added_node_gets_unique_id, test/test_quantization.py::TestNumericDebugger::test_simple, test/test_quantization.py::TestQuantizePT2E::test_allow_implicit_sharing, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_throw, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_transform_for_annotation, test/test_quantization.py::TestQuantizePT2E::test_conv_transpose_bn_relu, test/test_quantization.py::TestQuantizePT2E::test_dont_fold_other_constant, test/test_quantization.py::TestQuantizePT2E::test_embedding_conv_linear_quantization, test/test_quantization.py::TestQuantizePT2E::test_embedding_quantizer, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_observer_dedup, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_qat, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize_per_channel, test/test_quantization.py::TestQuantizePT2E::test_groupwise_per_channel_quant, test/test_quantization.py::TestQuantizePT2E::test_move_exported_model_bn_device_cpu, test/test_quantization.py::TestQuantizePT2E::test_preserve_nn_module_stack, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_float8_e5m2, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_int16, test/test_quantization.py::TestQuantizePT2E::test_reentrant, test/test_quantization.py::TestQuantizePT2E::test_save_load, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec, test/test_quantization.py::TestQuantizePT2E::test_transform_for_annotation, test/test_quantization.py::TestQuantizePT2E::test_wo_annotate_conv_output_quantizer, test/test_quantization.py::TestPT2ERepresentation::test_conv2d, test/test_quantization.py::TestXNNPACKQuantizer::test_add_mul_long, test/test_quantization.py::TestXNNPACKQuantizer::test_cat_same_node, test/test_quantization.py::TestXNNPACKQuantizer::test_conv1d, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear_int4_weight, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_gru, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_relu, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_with_dynamic_shape, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_and_inplace_mul, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_float32_max, test/test_quantization.py::TestXNNPACKQuantizer::test_obs_sharing_ops, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_type_case_2, test/test_quantization.py::TestXNNPACKQuantizerModels::test_resnet18, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_cat_recipe_same_inputs, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_flatten_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig_with_underscores, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_bias_derived_qspec, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_literal_args, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_preserve_source_fn_stack, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_literal_args, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_preserve_source_fn_stack, test/test_quantization.py::TestFXGraphMatcher::test_dict_return_type, test/test_quantization.py::TestFXGraphMatcher::test_op_relationship_mapping, test/test_quantization.py::TestFXGraphMatcher::test_simple_fun, test/test_quantization.py::TestFXGraphMatcher::test_simple_fusion, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_conv_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_mod, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_vs_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_weights, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_kwargs_shadow, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_shadow_activations_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_unsupported_op_copy_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_defined_function, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_extract_weights_linear, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_relu_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_logger_enabled_and_save_activations_flags, test/test_quantization.py::TestFXNumericSuiteNShadows::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_from_list, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_repr, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_resnet18, test/test_quantization.py::TestFxModelReportDetector::test_multi_linear_model_without_per_channel, test/test_quantization.py::TestFxModelReportDetectDynamicStatic::test_nested_detection_case, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_report_gen_empty, test/test_quantization.py::TestFxDetectOutliers::test_multiple_run_consistent_spike_outlier_report_gen, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_no_match, test/test_quantization.py::TestEqualizeFx::test_input_weight_eq_observer, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_prepare, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_results, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_weights_bias, test/test_quantization.py::TestSerialization::test_conv2d_graph, test/test_quantization.py::TestSerialization::test_conv2d_graph_v3, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph_v2, test/test_quantization.py::TestSerialization::test_linear_relu_package_quantization_transforms, test/test_quantization.py::TestQuantizeJit::test_conv_bn, test/test_quantization.py::TestQuantizeJit::test_single_linear, test/test_quantization.py::TestQuantizeJit::test_single_linear_dynamic, test/test_quantization.py::TestQuantizeJitPasses::test_conv_trace, test/test_quantization.py::TestQuantizeJitPasses::test_dedup_module_uses, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_complex_cases, test/test_quantization.py::TestQuantizeJitPasses::test_inplace_option, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_child_qconfig, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_reused_weight, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_skip_values, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant_shared_class_type, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequant_same_value, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequantize, test/test_quantization.py::TestQuantizeJitOps::test_clamp, test/test_quantization.py::TestQuantizeJitOps::test_group_norm, test/test_quantization.py::TestQuantizeJitOps::test_linear, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_convert_dynamic_fp16, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_shared_weights, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_cell_quantized, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_quantized, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_qconfig, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quant_type, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize_jit, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_utils, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_embedding_ops, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_utils, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_qat, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_linear_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_bn_relu, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_convert, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fusion_patterns, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_graph_module, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_pattern_utils, test/test_quantization.py::TestBitsCPU::test_cat_cpu, test/test_quantization.py::TestBitsCPU::test_subclass_cpu, test/test_quantization.py::TestBitsCPU::test_types_cpu, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cat_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_finfo_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_finfo_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_finfo_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPU::test_float8_e8m0fnu_rne_rounding_cpu, test/test_quantization.py::TestFloat8DtypeCPU::test_save_load_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_save_load_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_save_load_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_to_string_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_to_string_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_mul_cpu_float8_e4m3fn
2025-03-14T06:14:36.6403860Z 
2025-03-14T06:14:38.1413217Z Uploading artifacts took 1.53 seconds
2025-03-14T06:14:40.7395204Z Running test batch 'tests to run' cost 7530.6 seconds
2025-03-14T06:14:41.6545127Z 
2025-03-14T06:14:41.6545558Z real	125m35.503s
2025-03-14T06:14:41.6545894Z user	617m15.528s
2025-03-14T06:14:41.6546158Z sys	40m16.576s
2025-03-14T06:14:41.6546423Z + assert_git_not_dirty
2025-03-14T06:14:41.6546761Z + [[ linux-focal-py3.13-clang10 != *rocm* ]]
2025-03-14T06:14:41.6547174Z + [[ linux-focal-py3.13-clang10 != *xla* ]]
2025-03-14T06:14:41.6551858Z ++ git status --porcelain
2025-03-14T06:14:41.6552703Z ++ grep -v '?? third_party'
2025-03-14T06:15:05.2600337Z ++ true
2025-03-14T06:15:05.2600918Z + git_status=
2025-03-14T06:15:05.2601206Z + [[ -n '' ]]
2025-03-14T06:15:05.2601608Z + test_aten
2025-03-14T06:15:05.2601994Z + echo 'Running ATen tests with pytorch lib'
2025-03-14T06:15:05.2604333Z Running ATen tests with pytorch lib
2025-03-14T06:15:05.2605018Z + [[ -n '' ]]
2025-03-14T06:15:05.2605392Z + echo 'Running test with the build folder'
2025-03-14T06:15:05.2605764Z Running test with the build folder
2025-03-14T06:15:05.2606099Z + TEST_BASE_DIR=build/bin
2025-03-14T06:15:05.2606763Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin
2025-03-14T06:15:05.2631670Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin
2025-03-14T06:15:05.2641451Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin
2025-03-14T06:15:05.2650728Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin
2025-03-14T06:15:05.2661691Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorchbind_test.so build/bin
2025-03-14T06:15:05.2668594Z + ls build/bin
2025-03-14T06:15:05.2738000Z  BackoffTest			       cpu_profiling_allocator_test
2025-03-14T06:15:05.2738723Z  CMakeFiles			       cpu_rng_test
2025-03-14T06:15:05.2739353Z  CTestTestfile.cmake		       dispatch_key_set_test
2025-03-14T06:15:05.2740100Z  CppSignature_test		       dlconvertor_test
2025-03-14T06:15:05.2740568Z  Dict_test			       example_allreduce
2025-03-14T06:15:05.2741063Z  Dimname_test			       extension_backend_test
2025-03-14T06:15:05.2741468Z  FileStoreTest			       half_test
2025-03-14T06:15:05.2742102Z  HashStoreTest			       inline_container_test
2025-03-14T06:15:05.2742553Z  IListRef_test			       ivalue_test
2025-03-14T06:15:05.2742963Z  KernelFunction_test		       kernel_function_legacy_test
2025-03-14T06:15:05.2743386Z  List_test			       kernel_function_test
2025-03-14T06:15:05.2743776Z  MaybeOwned_test		       kernel_lambda_legacy_test
2025-03-14T06:15:05.2744205Z  NamedTensor_test		       kernel_lambda_test
2025-03-14T06:15:05.2744623Z  ProcessGroupGlooTest		       kernel_stackbased_test
2025-03-14T06:15:05.2745115Z  StorageUtils_test		       lazy_tensor_test
2025-03-14T06:15:05.2745537Z  TCPStoreTest			       legacy_vmap_test
2025-03-14T06:15:05.2745902Z  aot_model_compiler_test	       libc10.so
2025-03-14T06:15:05.2746263Z  apply_utils_test		      'libcaffe2*'
2025-03-14T06:15:05.2746601Z  atest				      'libmkldnn*'
2025-03-14T06:15:05.2746928Z  backend_fallback_test		      'libnccl*'
2025-03-14T06:15:05.2747272Z  basic				       libtorch.so
2025-03-14T06:15:05.2747595Z  broadcast_test			       libtorch_cpu.so
2025-03-14T06:15:05.2747966Z  c10_ArrayRef_test		       libtorch_global_deps.so
2025-03-14T06:15:05.2748364Z  c10_Bitset_test		       libtorch_python.so
2025-03-14T06:15:05.2749028Z  c10_CompileTimeFunctionPointer_test   libtorchbind_test.so
2025-03-14T06:15:05.2749575Z  c10_ConstexprCrc_test		       make_boxed_from_unboxed_functor_test
2025-03-14T06:15:05.2750072Z  c10_DeadlockDetection_test	       math_kernel_test
2025-03-14T06:15:05.2750498Z  c10_DeviceGuard_test		       memory_format_test
2025-03-14T06:15:05.2750915Z  c10_Device_test		       memory_overlapping_test
2025-03-14T06:15:05.2751336Z  c10_DispatchKeySet_test	       mobile_memory_cleanup
2025-03-14T06:15:05.2751720Z  c10_Half_test			       native_test
2025-03-14T06:15:05.2752089Z  c10_InlineDeviceGuard_test	       op_allowlist_test
2025-03-14T06:15:05.2752533Z  c10_InlineStreamGuard_test	       op_registration_test
2025-03-14T06:15:05.2752956Z  c10_LeftRight_test		       operator_name_test
2025-03-14T06:15:05.2753359Z  c10_Metaprogramming_test	       operators_test
2025-03-14T06:15:05.2753794Z  c10_NetworkFlow_test		       packedtensoraccessor_test
2025-03-14T06:15:05.2754214Z  c10_Scalar_test		       parallel_benchmark
2025-03-14T06:15:05.2754589Z  c10_SizesAndStrides_test	       pow_test
2025-03-14T06:15:05.2754952Z  c10_StreamGuard_test		       protoc
2025-03-14T06:15:05.2755298Z  c10_SymInt_test		       protoc-3.13.0.0
2025-03-14T06:15:05.2755670Z  c10_Synchronized_test		       quantized_test
2025-03-14T06:15:05.2756061Z  c10_ThreadLocal_test		       reduce_ops_test
2025-03-14T06:15:05.2756542Z  c10_TypeIndex_test		       reportMemoryUsage_test
2025-03-14T06:15:05.2756954Z  c10_TypeList_test		       scalar_tensor_test
2025-03-14T06:15:05.2757327Z  c10_TypeTraits_test		       scalar_test
2025-03-14T06:15:05.2757709Z  c10_accumulate_test		       static_runtime_bench
2025-03-14T06:15:05.2758112Z  c10_bfloat16_test		       static_runtime_test
2025-03-14T06:15:05.2758489Z  c10_bit_cast_test		       stride_properties_test
2025-03-14T06:15:05.2758908Z  c10_complex_math_test		       tensor_iterator_test
2025-03-14T06:15:05.2759359Z  c10_complex_test		       test_api
2025-03-14T06:15:05.2759690Z  c10_cow_test			       test_cpp_rpc
2025-03-14T06:15:05.2760032Z  c10_error_test			       test_dist_autograd
2025-03-14T06:15:05.2760434Z  c10_exception_test		       test_edge_op_registration
2025-03-14T06:15:05.2760826Z  c10_flags_test			       test_jit
2025-03-14T06:15:05.2761156Z  c10_generic_math_test		       test_lazy
2025-03-14T06:15:05.2761529Z  c10_intrusive_ptr_benchmark	       test_mobile_nnc
2025-03-14T06:15:05.2761936Z  c10_intrusive_ptr_test		       test_parallel
2025-03-14T06:15:05.2762309Z  c10_irange_test		       test_tensorexpr
2025-03-14T06:15:05.2762655Z  c10_lazy_test			       thread_init_test
2025-03-14T06:15:05.2763008Z  c10_logging_test		       torch_shm_manager
2025-03-14T06:15:05.2763443Z  c10_optional_test		       tutorial_tensorexpr
2025-03-14T06:15:05.2763853Z  c10_ordered_preserving_dict_test      type_ptr_test
2025-03-14T06:15:05.2764237Z  c10_registry_test		       type_test
2025-03-14T06:15:05.2764608Z  c10_small_vector_test		       undefined_tensor_test
2025-03-14T06:15:05.2765011Z  c10_ssize_test			       vec_test_all_types_AVX2
2025-03-14T06:15:05.2765408Z  c10_string_util_test		       vec_test_all_types_AVX512
2025-03-14T06:15:05.2765845Z  c10_string_view_test		       vec_test_all_types_DEFAULT
2025-03-14T06:15:05.2766267Z  c10_tempfile_test		       verify_api_visibility
2025-03-14T06:15:05.2766643Z  c10_typeid_test		       weakref_test
2025-03-14T06:15:05.2767047Z  cmake_install.cmake		       wrapdim_test
2025-03-14T06:15:05.2767458Z  cpu_allocator_test		       xla_tensor_test
2025-03-14T06:15:05.2767799Z  cpu_generator_test
2025-03-14T06:15:05.2768082Z + aten/tools/run_tests.sh build/bin
2025-03-14T06:15:05.2769097Z + set -e
2025-03-14T06:15:05.2771963Z ++ dirname aten/tools/run_tests.sh
2025-03-14T06:15:05.2787920Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup
2025-03-14T06:15:05.2788466Z + export CPP_TESTS_DIR=build/bin
2025-03-14T06:15:05.2788775Z + CPP_TESTS_DIR=build/bin
2025-03-14T06:15:05.2789318Z + VALGRIND=ON
2025-03-14T06:15:05.2791343Z + python test/run_test.py --cpp --verbose -i cpp/basic cpp/atest cpp/scalar_test cpp/broadcast_test cpp/wrapdim_test cpp/apply_utils_test cpp/dlconvertor_test cpp/native_test cpp/scalar_tensor_test cpp/undefined_tensor_test cpp/extension_backend_test cpp/lazy_tensor_test cpp/tensor_iterator_test cpp/Dimname_test cpp/Dict_test cpp/NamedTensor_test cpp/cpu_generator_test cpp/legacy_vmap_test cpp/operators_test
2025-03-14T06:15:05.3814611Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-14T06:15:05.3815536Z   import pkg_resources
2025-03-14T06:15:09.1686476Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-14T06:15:09.1858505Z Found test times from artifacts
2025-03-14T06:15:09.2571968Z Found test times from artifacts
2025-03-14T06:15:09.2597632Z Running all tests
2025-03-14T06:15:09.2602293Z Running parallel tests on 3 processes
2025-03-14T06:15:09.2603880Z Name: tests to run (est. time: 0.0min)
2025-03-14T06:15:09.2604247Z   Serial tests (0):
2025-03-14T06:15:09.2604522Z   Parallel tests (19):
2025-03-14T06:15:09.2604822Z     cpp/Dict_test 1/1
2025-03-14T06:15:09.2605099Z     cpp/Dimname_test 1/1
2025-03-14T06:15:09.2605396Z     cpp/NamedTensor_test 1/1
2025-03-14T06:15:09.2605891Z     cpp/apply_utils_test 1/1
2025-03-14T06:15:09.2606202Z     cpp/atest 1/1
2025-03-14T06:15:09.2606461Z     cpp/basic 1/1
2025-03-14T06:15:09.2606734Z     cpp/broadcast_test 1/1
2025-03-14T06:15:09.2607039Z     cpp/cpu_generator_test 1/1
2025-03-14T06:15:09.2607352Z     cpp/dlconvertor_test 1/1
2025-03-14T06:15:09.2607673Z     cpp/extension_backend_test 1/1
2025-03-14T06:15:09.2607997Z     cpp/lazy_tensor_test 1/1
2025-03-14T06:15:09.2608299Z     cpp/legacy_vmap_test 1/1
2025-03-14T06:15:09.2608598Z     cpp/native_test 1/1
2025-03-14T06:15:09.2608867Z     cpp/operators_test 1/1
2025-03-14T06:15:09.2609172Z     cpp/scalar_tensor_test 1/1
2025-03-14T06:15:09.2609472Z     cpp/scalar_test 1/1
2025-03-14T06:15:09.2609759Z     cpp/tensor_iterator_test 1/1
2025-03-14T06:15:09.2610082Z     cpp/undefined_tensor_test 1/1
2025-03-14T06:15:09.2610400Z     cpp/wrapdim_test 1/1
2025-03-14T06:15:09.2610700Z Name: excluded (est. time: 0.0min)
2025-03-14T06:15:09.2611016Z   Serial tests (0):
2025-03-14T06:15:09.2611285Z   Parallel tests (0):
2025-03-14T06:15:09.2658682Z Running cpp/Dict_test 1/1 ... [2025-03-14 06:15:09.265606]
2025-03-14T06:15:09.2659110Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:09.2663598Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-665d279c84192f5f.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:09.266082]
2025-03-14T06:15:11.0336768Z 
2025-03-14T06:15:11.0337935Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_9b04b0256286fe2f_.log
2025-03-14T06:15:11.0338656Z 
2025-03-14T06:15:11.0338878Z Running cpp/Dimname_test 1/1 ... [2025-03-14 06:15:11.033645]
2025-03-14T06:15:11.0339305Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:11.0342366Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3233300dfa87549a.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:11.033977]
2025-03-14T06:15:12.7009637Z 
2025-03-14T06:15:12.7010936Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_b62b46cd5b94f744_.log
2025-03-14T06:15:12.7011970Z 
2025-03-14T06:15:12.7012335Z Running cpp/NamedTensor_test 1/1 ... [2025-03-14 06:15:12.700811]
2025-03-14T06:15:12.7013009Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:12.7015210Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d2711fefcdd6dbc8.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:12.701203]
2025-03-14T06:15:14.3179348Z 
2025-03-14T06:15:14.3180344Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_3ac537fd39c42204_.log
2025-03-14T06:15:14.3181132Z 
2025-03-14T06:15:14.3181390Z Running cpp/apply_utils_test 1/1 ... [2025-03-14 06:15:14.317805]
2025-03-14T06:15:14.3181837Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:14.3183496Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-794a8d132f3b2a94.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:14.318100]
2025-03-14T06:15:15.9348416Z 
2025-03-14T06:15:15.9349483Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_d636eafd305cf701_.log
2025-03-14T06:15:15.9350172Z 
2025-03-14T06:15:15.9350343Z Running cpp/atest 1/1 ... [2025-03-14 06:15:15.934560]
2025-03-14T06:15:15.9350792Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:15.9352435Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d99666ffec3857cd.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:15.934964]
2025-03-14T06:15:17.5515977Z 
2025-03-14T06:15:17.5517030Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_9dafa543c916433b_.log
2025-03-14T06:15:17.5517622Z 
2025-03-14T06:15:17.5517800Z Running cpp/basic 1/1 ... [2025-03-14 06:15:17.551440]
2025-03-14T06:15:17.5518210Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:17.5520059Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-fa115f71b92bf942.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:17.551800]
2025-03-14T06:15:19.1684466Z 
2025-03-14T06:15:19.1685570Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_2a5b0b058872c967_.log
2025-03-14T06:15:19.1686266Z 
2025-03-14T06:15:19.1686470Z Running cpp/broadcast_test 1/1 ... [2025-03-14 06:15:19.168309]
2025-03-14T06:15:19.1686974Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:19.1689009Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d64723b723efb873.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:19.168663]
2025-03-14T06:15:20.8357351Z 
2025-03-14T06:15:20.8358339Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_40b1d8fb57e1536b_.log
2025-03-14T06:15:20.8359067Z 
2025-03-14T06:15:20.8359345Z Running cpp/cpu_generator_test 1/1 ... [2025-03-14 06:15:20.835596]
2025-03-14T06:15:20.8359860Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:20.8361785Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f2bd59b4ee771128.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:20.835949]
2025-03-14T06:15:22.5030393Z 
2025-03-14T06:15:22.5031834Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_0a9ffc06724ebabc_.log
2025-03-14T06:15:22.5032885Z 
2025-03-14T06:15:22.5033110Z Running cpp/dlconvertor_test 1/1 ... [2025-03-14 06:15:22.502753]
2025-03-14T06:15:22.5033555Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:22.5034758Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-62377b90928dc283.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:22.503071]
2025-03-14T06:15:24.1697313Z 
2025-03-14T06:15:24.1699058Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_751dfb49effe3093_.log
2025-03-14T06:15:24.1700202Z 
2025-03-14T06:15:24.1700581Z Running cpp/extension_backend_test 1/1 ... [2025-03-14 06:15:24.169583]
2025-03-14T06:15:24.1701294Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:24.1703509Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-dcc57e04775dece2.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:24.170029]
2025-03-14T06:15:25.7871541Z 
2025-03-14T06:15:25.7872513Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_0eeda045d037c53a_.log
2025-03-14T06:15:25.7873262Z 
2025-03-14T06:15:25.7873484Z Running cpp/lazy_tensor_test 1/1 ... [2025-03-14 06:15:25.787038]
2025-03-14T06:15:25.7874300Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:25.7875811Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d692ec6cc0db25bb.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:25.787364]
2025-03-14T06:15:27.4542835Z 
2025-03-14T06:15:27.4543768Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_ef0da0289a9b3b9c_.log
2025-03-14T06:15:27.4544437Z 
2025-03-14T06:15:27.4544658Z Running cpp/legacy_vmap_test 1/1 ... [2025-03-14 06:15:27.454152]
2025-03-14T06:15:27.4545100Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:27.4546905Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-37e03d09ac4be116.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:27.454485]
2025-03-14T06:15:29.1212767Z 
2025-03-14T06:15:29.1213807Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_8e3c3810b4e61187_.log
2025-03-14T06:15:29.1214492Z 
2025-03-14T06:15:29.1214895Z Running cpp/native_test 1/1 ... [2025-03-14 06:15:29.121064]
2025-03-14T06:15:29.1215457Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:29.1216679Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-50f97b6e01a6a1cf.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:29.121416]
2025-03-14T06:15:30.7881989Z 
2025-03-14T06:15:30.7883218Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_1c43c455f78baaba_.log
2025-03-14T06:15:30.7883849Z 
2025-03-14T06:15:30.7884110Z Running cpp/operators_test 1/1 ... [2025-03-14 06:15:30.788054]
2025-03-14T06:15:30.7884919Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:30.7886416Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e2f11ff3ef38fd20.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:30.788356]
2025-03-14T06:15:32.4050005Z 
2025-03-14T06:15:32.4050947Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_42becebc87d8426a_.log
2025-03-14T06:15:32.4051628Z 
2025-03-14T06:15:32.4051850Z Running cpp/scalar_tensor_test 1/1 ... [2025-03-14 06:15:32.404834]
2025-03-14T06:15:32.4052302Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:32.4053757Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0d8f5781e4e70b70.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:32.405172]
2025-03-14T06:15:34.0219095Z 
2025-03-14T06:15:34.0219992Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_ed5419613d1c5230_.log
2025-03-14T06:15:34.0220700Z 
2025-03-14T06:15:34.0220922Z Running cpp/scalar_test 1/1 ... [2025-03-14 06:15:34.021763]
2025-03-14T06:15:34.0221348Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:34.0223398Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9838bcf9a7736b17.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:34.022105]
2025-03-14T06:15:35.6388137Z 
2025-03-14T06:15:35.6389047Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_66f6038397909da8_.log
2025-03-14T06:15:35.6389918Z 
2025-03-14T06:15:35.6390158Z Running cpp/tensor_iterator_test 1/1 ... [2025-03-14 06:15:35.638636]
2025-03-14T06:15:35.6390628Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:35.6391930Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5a4c12baf474bbcc.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:35.638974]
2025-03-14T06:15:37.2555737Z 
2025-03-14T06:15:37.2557003Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_7bb61ae30457479a_.log
2025-03-14T06:15:37.2557729Z 
2025-03-14T06:15:37.2558022Z Running cpp/undefined_tensor_test 1/1 ... [2025-03-14 06:15:37.255424]
2025-03-14T06:15:37.2558500Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:37.2560274Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-718bf63d8bf14548.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:37.255760]
2025-03-14T06:15:38.8724316Z 
2025-03-14T06:15:38.8725494Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_5841a25b3bcd31a0_.log
2025-03-14T06:15:38.8726250Z 
2025-03-14T06:15:38.8726487Z Running cpp/wrapdim_test 1/1 ... [2025-03-14 06:15:38.872292]
2025-03-14T06:15:38.8726921Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:38.8728169Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5e10f60cbf45c7a0.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:38.872613]
2025-03-14T06:15:40.4894555Z 
2025-03-14T06:15:40.4895703Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_d9274ddde0c6e5b5_.log
2025-03-14T06:15:40.4896431Z 
2025-03-14T06:15:40.4905727Z Running cpp/Dict_test 1/1 ... [2025-03-14 06:15:40.490313]
2025-03-14T06:15:40.4906487Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:40.4907183Z Running cpp/Dimname_test 1/1 ... [2025-03-14 06:15:40.490501]
2025-03-14T06:15:40.4907827Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:40.4908512Z Running cpp/NamedTensor_test 1/1 ... [2025-03-14 06:15:40.490553]
2025-03-14T06:15:40.4909188Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:40.4911178Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e4b6e160c79af61f.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:40.490798]
2025-03-14T06:15:40.4914841Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1ba53aada6a8e8d4.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:40.490988]
2025-03-14T06:15:40.4917173Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4765f48af6833717.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:40.491114]
2025-03-14T06:15:43.1601605Z 
2025-03-14T06:15:43.1602708Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_e8ff230edb1ae1ab_.log
2025-03-14T06:15:43.1603388Z 
2025-03-14T06:15:43.8616830Z 
2025-03-14T06:15:43.8617819Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_70797620bb0190af_.log
2025-03-14T06:15:43.8618508Z 
2025-03-14T06:15:46.8351675Z Running cpp/apply_utils_test 1/1 ... [2025-03-14 06:15:46.834705]
2025-03-14T06:15:46.8352926Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:46.8355419Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-bb7bc73b213bb3b0.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:46.835198]
2025-03-14T06:15:47.5772351Z Running cpp/atest 1/1 ... [2025-03-14 06:15:47.576755]
2025-03-14T06:15:47.5772855Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:47.5775293Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-98d34401da3c8a6d.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:47.577261]
2025-03-14T06:15:48.6691932Z 
2025-03-14T06:15:48.6693025Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_dd0a9f179b61c666_.log
2025-03-14T06:15:48.6693917Z 
2025-03-14T06:15:49.4546588Z 
2025-03-14T06:15:49.4547559Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_0d083e752dcb84a7_.log
2025-03-14T06:15:49.4548239Z 
2025-03-14T06:15:51.6485966Z 
2025-03-14T06:15:51.6487070Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_c72c978ce801a608_.log
2025-03-14T06:15:51.6487690Z 
2025-03-14T06:15:52.4060491Z Running cpp/basic 1/1 ... [2025-03-14 06:15:52.405738]
2025-03-14T06:15:52.4060955Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:52.4063783Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2c0874781dcf62d6.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:52.406150]
2025-03-14T06:15:53.1480320Z Running cpp/broadcast_test 1/1 ... [2025-03-14 06:15:53.147628]
2025-03-14T06:15:53.1481480Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:53.1483927Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-a0d16688c4d5f6e8.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:53.148050]
2025-03-14T06:15:54.9747225Z 
2025-03-14T06:15:54.9748103Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_b145df72707a4a2d_.log
2025-03-14T06:15:54.9748851Z 
2025-03-14T06:15:55.3661950Z 
2025-03-14T06:15:55.3663130Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_3f7fd59109af0371_.log
2025-03-14T06:15:55.3663915Z 
2025-03-14T06:15:55.3792146Z Running cpp/cpu_generator_test 1/1 ... [2025-03-14 06:15:55.378944]
2025-03-14T06:15:55.3792664Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:55.3795809Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-26ce1ebea7d1631a.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:55.379345]
2025-03-14T06:15:58.6417836Z Running cpp/dlconvertor_test 1/1 ... [2025-03-14 06:15:58.641410]
2025-03-14T06:15:58.6418321Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:58.6421413Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1d864d978782b5f6.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:58.641861]
2025-03-14T06:15:59.0810331Z Running cpp/extension_backend_test 1/1 ... [2025-03-14 06:15:59.080626]
2025-03-14T06:15:59.0811227Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:15:59.0813672Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-10715263599f8749.xml', '-x', '--reruns=2'] ... [2025-03-14 06:15:59.081044]
2025-03-14T06:15:59.3998762Z 
2025-03-14T06:15:59.3999767Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_3b20c0631177b86a_.log
2025-03-14T06:15:59.4000473Z 
2025-03-14T06:16:00.7098172Z 
2025-03-14T06:16:00.7099161Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_43520de612e47345_.log
2025-03-14T06:16:00.7099840Z 
2025-03-14T06:16:01.1989166Z 
2025-03-14T06:16:01.1990158Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_37048d91051125dc_.log
2025-03-14T06:16:01.1990901Z 
2025-03-14T06:16:03.1311737Z Running cpp/lazy_tensor_test 1/1 ... [2025-03-14 06:16:03.130843]
2025-03-14T06:16:03.1312258Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:03.1315167Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3f245f9f934592aa.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:03.131256]
2025-03-14T06:16:04.3506223Z Running cpp/legacy_vmap_test 1/1 ... [2025-03-14 06:16:04.350264]
2025-03-14T06:16:04.3506724Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:04.3509458Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d2ce491c33ca3eb2.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:04.350702]
2025-03-14T06:16:04.8445998Z Running cpp/native_test 1/1 ... [2025-03-14 06:16:04.844171]
2025-03-14T06:16:04.8446814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:04.8449660Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-dd91ae732b2045fd.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:04.844601]
2025-03-14T06:16:05.1988606Z 
2025-03-14T06:16:05.1989834Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_ac60d4fcd07b6801_.log
2025-03-14T06:16:05.1990512Z 
2025-03-14T06:16:07.0629314Z 
2025-03-14T06:16:07.0630699Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_ee0548a915876df9_.log
2025-03-14T06:16:07.0631947Z 
2025-03-14T06:16:08.9464031Z Running cpp/operators_test 1/1 ... [2025-03-14 06:16:08.946028]
2025-03-14T06:16:08.9464539Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:08.9467158Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9ed7749f4f74efe5.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:08.946456]
2025-03-14T06:16:09.1230144Z 
2025-03-14T06:16:09.1231294Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_d98baff483857014_.log
2025-03-14T06:16:09.1231964Z 
2025-03-14T06:16:10.7095762Z Running cpp/scalar_tensor_test 1/1 ... [2025-03-14 06:16:10.709166]
2025-03-14T06:16:10.7096496Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:10.7099548Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1e9c6ed13d791e8a.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:10.709648]
2025-03-14T06:16:11.4151415Z 
2025-03-14T06:16:11.4153063Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_b2b65da0de60554c_.log
2025-03-14T06:16:11.4154154Z 
2025-03-14T06:16:12.8747953Z Running cpp/scalar_test 1/1 ... [2025-03-14 06:16:12.874403]
2025-03-14T06:16:12.8748959Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:12.8751484Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-216f4e86d0cc8ff1.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:12.874810]
2025-03-14T06:16:12.8775081Z 
2025-03-14T06:16:12.8776371Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_a7006d0089278242_.log
2025-03-14T06:16:12.8777625Z 
2025-03-14T06:16:15.0757001Z Running cpp/tensor_iterator_test 1/1 ... [2025-03-14 06:16:15.075331]
2025-03-14T06:16:15.0757560Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:15.0760546Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-735e79d733db78e4.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:15.075733]
2025-03-14T06:16:15.2936006Z 
2025-03-14T06:16:15.2936940Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_73d307658383e093_.log
2025-03-14T06:16:15.2937579Z 
2025-03-14T06:16:16.6021476Z Running cpp/undefined_tensor_test 1/1 ... [2025-03-14 06:16:16.601732]
2025-03-14T06:16:16.6022408Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:16.6028612Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0df7203c55c43acf.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:16.602483]
2025-03-14T06:16:18.7015375Z 
2025-03-14T06:16:18.7016883Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_4e1e4cdfa1ba933b_.log
2025-03-14T06:16:18.7018217Z 
2025-03-14T06:16:18.9088062Z Running cpp/wrapdim_test 1/1 ... [2025-03-14 06:16:18.908379]
2025-03-14T06:16:18.9088807Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:16:18.9091258Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-150dcc55b242ab63.xml', '-x', '--reruns=2'] ... [2025-03-14 06:16:18.908822]
2025-03-14T06:16:21.0773403Z 
2025-03-14T06:16:21.0775001Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_ed451665cc644e6f_.log
2025-03-14T06:16:21.0776498Z 
2025-03-14T06:16:25.1563940Z 
2025-03-14T06:16:25.1565002Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_830e43d68af6aedb_.log
2025-03-14T06:16:25.1565729Z 
2025-03-14T06:16:25.9589305Z Running test batch 'tests to run' cost 76.7 seconds
2025-03-14T06:16:26.6612392Z + run_if_exists tensor_interop_test
2025-03-14T06:16:26.6613005Z + local test_name=tensor_interop_test
2025-03-14T06:16:26.6613554Z + [[ -x build/bin/tensor_interop_test ]]
2025-03-14T06:16:26.6614068Z + echo 'Warning: tensor_interop_test does not exist.'
2025-03-14T06:16:26.6614642Z Warning: tensor_interop_test does not exist.
2025-03-14T06:16:26.6615113Z + run_if_exists cudnn_test
2025-03-14T06:16:26.6615509Z + local test_name=cudnn_test
2025-03-14T06:16:26.6615975Z + [[ -x build/bin/cudnn_test ]]
2025-03-14T06:16:26.6616420Z + echo 'Warning: cudnn_test does not exist.'
2025-03-14T06:16:26.6616918Z Warning: cudnn_test does not exist.
2025-03-14T06:16:26.6617326Z + run_if_exists cuda_generator_test
2025-03-14T06:16:26.6618756Z + local test_name=cuda_generator_test
2025-03-14T06:16:26.6619113Z + [[ -x build/bin/cuda_generator_test ]]
2025-03-14T06:16:26.6619505Z + echo 'Warning: cuda_generator_test does not exist.'
2025-03-14T06:16:26.6619907Z Warning: cuda_generator_test does not exist.
2025-03-14T06:16:26.6620281Z + run_if_exists apply_test
2025-03-14T06:16:26.6620581Z + local test_name=apply_test
2025-03-14T06:16:26.6620891Z + [[ -x build/bin/apply_test ]]
2025-03-14T06:16:26.6621225Z + echo 'Warning: apply_test does not exist.'
2025-03-14T06:16:26.6621583Z Warning: apply_test does not exist.
2025-03-14T06:16:26.6621908Z + run_if_exists stream_test
2025-03-14T06:16:26.6622206Z + local test_name=stream_test
2025-03-14T06:16:26.6622516Z + [[ -x build/bin/stream_test ]]
2025-03-14T06:16:26.6622851Z + echo 'Warning: stream_test does not exist.'
2025-03-14T06:16:26.6623215Z Warning: stream_test does not exist.
2025-03-14T06:16:26.6623544Z + run_if_exists cuda_half_test
2025-03-14T06:16:26.6623862Z + local test_name=cuda_half_test
2025-03-14T06:16:26.6624183Z + [[ -x build/bin/cuda_half_test ]]
2025-03-14T06:16:26.6624539Z + echo 'Warning: cuda_half_test does not exist.'
2025-03-14T06:16:26.6624914Z Warning: cuda_half_test does not exist.
2025-03-14T06:16:26.6625260Z + run_if_exists cuda_vectorized_test
2025-03-14T06:16:26.6625681Z + local test_name=cuda_vectorized_test
2025-03-14T06:16:26.6626036Z + [[ -x build/bin/cuda_vectorized_test ]]
2025-03-14T06:16:26.6626434Z + echo 'Warning: cuda_vectorized_test does not exist.'
2025-03-14T06:16:26.6626852Z Warning: cuda_vectorized_test does not exist.
2025-03-14T06:16:26.6627227Z + run_if_exists cuda_distributions_test
2025-03-14T06:16:26.6627584Z + local test_name=cuda_distributions_test
2025-03-14T06:16:26.6627951Z + [[ -x build/bin/cuda_distributions_test ]]
2025-03-14T06:16:26.6628388Z + echo 'Warning: cuda_distributions_test does not exist.'
2025-03-14T06:16:26.6628827Z Warning: cuda_distributions_test does not exist.
2025-03-14T06:16:26.6629212Z + run_if_exists cuda_optional_test
2025-03-14T06:16:26.6629607Z + local test_name=cuda_optional_test
2025-03-14T06:16:26.6630007Z + [[ -x build/bin/cuda_optional_test ]]
2025-03-14T06:16:26.6630397Z + echo 'Warning: cuda_optional_test does not exist.'
2025-03-14T06:16:26.6630808Z Warning: cuda_optional_test does not exist.
2025-03-14T06:16:26.6631180Z + run_if_exists cuda_tensor_interop_test
2025-03-14T06:16:26.6631545Z + local test_name=cuda_tensor_interop_test
2025-03-14T06:16:26.6631916Z + [[ -x build/bin/cuda_tensor_interop_test ]]
2025-03-14T06:16:26.6632339Z + echo 'Warning: cuda_tensor_interop_test does not exist.'
2025-03-14T06:16:26.6632779Z Warning: cuda_tensor_interop_test does not exist.
2025-03-14T06:16:26.6633160Z + run_if_exists cuda_complex_test
2025-03-14T06:16:26.6633487Z + local test_name=cuda_complex_test
2025-03-14T06:16:26.6633810Z + [[ -x build/bin/cuda_complex_test ]]
2025-03-14T06:16:26.6634186Z + echo 'Warning: cuda_complex_test does not exist.'
2025-03-14T06:16:26.6634588Z Warning: cuda_complex_test does not exist.
2025-03-14T06:16:26.6634957Z + run_if_exists cuda_complex_math_test
2025-03-14T06:16:26.6635309Z + local test_name=cuda_complex_math_test
2025-03-14T06:16:26.6635671Z + [[ -x build/bin/cuda_complex_math_test ]]
2025-03-14T06:16:26.6636077Z + echo 'Warning: cuda_complex_math_test does not exist.'
2025-03-14T06:16:26.6636506Z Warning: cuda_complex_math_test does not exist.
2025-03-14T06:16:26.6636876Z + run_if_exists cuda_cub_test
2025-03-14T06:16:26.6637193Z + local test_name=cuda_cub_test
2025-03-14T06:16:26.6637517Z + [[ -x build/bin/cuda_cub_test ]]
2025-03-14T06:16:26.6637876Z + echo 'Warning: cuda_cub_test does not exist.'
2025-03-14T06:16:26.6638259Z Warning: cuda_cub_test does not exist.
2025-03-14T06:16:26.6638610Z + run_if_exists cuda_atomic_ops_test
2025-03-14T06:16:26.6638958Z + local test_name=cuda_atomic_ops_test
2025-03-14T06:16:26.6639412Z + [[ -x build/bin/cuda_atomic_ops_test ]]
2025-03-14T06:16:26.6639813Z + echo 'Warning: cuda_atomic_ops_test does not exist.'
2025-03-14T06:16:26.6640266Z Warning: cuda_atomic_ops_test does not exist.
2025-03-14T06:16:26.6640623Z + '[' ON == ON ']'
2025-03-14T06:16:26.6641343Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA'
2025-03-14T06:16:26.6914474Z ==130358== Memcheck, a memory error detector
2025-03-14T06:16:26.6914962Z ==130358== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al.
2025-03-14T06:16:26.6915561Z ==130358== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info
2025-03-14T06:16:26.6916091Z ==130358== Command: build/bin/basic --gtest_filter=-*CUDA
2025-03-14T06:16:26.6916472Z ==130358== 
2025-03-14T06:16:27.1939608Z ==130358== Warning: set address range perms: large range [0x4a08000, 0x15af9000) (defined)
2025-03-14T06:16:55.8129281Z Running main() from ../third_party/googletest/googletest/src/gtest_main.cc
2025-03-14T06:16:55.8405824Z Note: Google Test filter = -*CUDA
2025-03-14T06:16:55.8452652Z [==========] Running 4 tests from 1 test suite.
2025-03-14T06:16:55.8479325Z [----------] Global test environment set-up.
2025-03-14T06:16:55.8550083Z [----------] 4 tests from BasicTest
2025-03-14T06:16:55.8574353Z [ RUN      ] BasicTest.BasicTestCPU
2025-03-14T06:16:57.4799378Z    571 ms
2025-03-14T06:16:57.5647054Z    53 ms
2025-03-14T06:16:57.6386213Z    65 ms
2025-03-14T06:16:58.3359698Z [       OK ] BasicTest.BasicTestCPU (2476 ms)
2025-03-14T06:16:58.3367933Z [ RUN      ] BasicTest.BasicTestHalfCPU
2025-03-14T06:16:58.5225644Z    136 ms
2025-03-14T06:16:58.5728483Z    45 ms
2025-03-14T06:16:58.6393850Z    64 ms
2025-03-14T06:16:58.6927024Z [       OK ] BasicTest.BasicTestHalfCPU (350 ms)
2025-03-14T06:16:58.6927484Z [ RUN      ] BasicTest.FactoryMethodsTest
2025-03-14T06:16:58.7252081Z [       OK ] BasicTest.FactoryMethodsTest (32 ms)
2025-03-14T06:16:58.7252492Z [ RUN      ] BasicTest.BasicStdTestCPU
2025-03-14T06:16:58.8501768Z Simple example: called once
2025-03-14T06:16:58.9410387Z throw: call_once will retry
2025-03-14T06:16:58.9422923Z throw: call_once will retry
2025-03-14T06:16:58.9426431Z Didn't throw, call_once will not attempt again
2025-03-14T06:16:58.9447363Z [       OK ] BasicTest.BasicStdTestCPU (219 ms)
2025-03-14T06:16:58.9470983Z [----------] 4 tests from BasicTest (3088 ms total)
2025-03-14T06:16:58.9471278Z 
2025-03-14T06:16:58.9484989Z [----------] Global test environment tear-down
2025-03-14T06:16:58.9516737Z [==========] 4 tests from 1 test suite ran. (3114 ms total)
2025-03-14T06:16:58.9527640Z [  PASSED  ] 4 tests.
2025-03-14T06:17:00.7697519Z ==130358== 
2025-03-14T06:17:00.7701052Z ==130358== HEAP SUMMARY:
2025-03-14T06:17:00.7701443Z ==130358==     in use at exit: 241,528 bytes in 3,996 blocks
2025-03-14T06:17:00.7701995Z ==130358==   total heap usage: 754,430 allocs, 750,434 frees, 215,174,995 bytes allocated
2025-03-14T06:17:00.7702474Z ==130358== 
2025-03-14T06:17:00.8081340Z ==130358== LEAK SUMMARY:
2025-03-14T06:17:00.8081706Z ==130358==    definitely lost: 0 bytes in 0 blocks
2025-03-14T06:17:00.8082141Z ==130358==    indirectly lost: 0 bytes in 0 blocks
2025-03-14T06:17:00.8082555Z ==130358==      possibly lost: 0 bytes in 0 blocks
2025-03-14T06:17:00.8082966Z ==130358==    still reachable: 241,528 bytes in 3,996 blocks
2025-03-14T06:17:00.8083384Z ==130358==         suppressed: 0 bytes in 0 blocks
2025-03-14T06:17:00.8083876Z ==130358== Rerun with --leak-check=full to see details of leaked memory
2025-03-14T06:17:00.8084303Z ==130358== 
2025-03-14T06:17:00.8084653Z ==130358== For lists of detected and suppressed errors, rerun with: -s
2025-03-14T06:17:00.8085207Z ==130358== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)
2025-03-14T06:17:00.8570521Z + [[ -x build/bin/tensor_interop_test ]]
2025-03-14T06:17:00.8572427Z + [[ -n '' ]]
2025-03-14T06:17:00.8572686Z + assert_git_not_dirty
2025-03-14T06:17:00.8572999Z + [[ linux-focal-py3.13-clang10 != *rocm* ]]
2025-03-14T06:17:00.8573378Z + [[ linux-focal-py3.13-clang10 != *xla* ]]
2025-03-14T06:17:00.8578850Z ++ git status --porcelain
2025-03-14T06:17:00.8579884Z ++ grep -v '?? third_party'
2025-03-14T06:17:01.0795360Z ++ true
2025-03-14T06:17:01.0795885Z + git_status=
2025-03-14T06:17:01.0796169Z + [[ -n '' ]]
2025-03-14T06:17:01.0796430Z + test_libtorch 1
2025-03-14T06:17:01.0796694Z + local SHARD=1
2025-03-14T06:17:01.0796986Z + [[ default != \s\l\o\w ]]
2025-03-14T06:17:01.0797303Z + echo 'Testing libtorch'
2025-03-14T06:17:01.0797601Z Testing libtorch
2025-03-14T06:17:01.0798410Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libbackend_with_compiler.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0809387Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libjitbackend_test.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0818592Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2_nvrtc.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0828011Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0837518Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libshm.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0849669Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorchbind_test.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0858411Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnvfuser*' /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0867290Z + export CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0868081Z + CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-14T06:17:01.0868565Z + [[ -z 1 ]]
2025-03-14T06:17:01.0868806Z + [[ 1 == \1 ]]
2025-03-14T06:17:01.0869068Z + test_libtorch_api
2025-03-14T06:17:01.0869412Z + MNIST_DIR=/var/lib/jenkins/workspace/test/cpp/api/mnist
2025-03-14T06:17:01.0870006Z + python tools/download_mnist.py --quiet -d /var/lib/jenkins/workspace/test/cpp/api/mnist
2025-03-14T06:17:01.1345706Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ...
2025-03-14T06:17:01.1958026Z Failed to download (trying next):
2025-03-14T06:17:01.1958418Z HTTP Error 404: Not Found
2025-03-14T06:17:01.1958955Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz ...
2025-03-14T06:17:01.5223047Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ...
2025-03-14T06:17:01.5279197Z Failed to download (trying next):
2025-03-14T06:17:01.5279586Z HTTP Error 404: Not Found
2025-03-14T06:17:01.5280113Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz ...
2025-03-14T06:17:01.5735354Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ...
2025-03-14T06:17:01.5784638Z Failed to download (trying next):
2025-03-14T06:17:01.5785015Z HTTP Error 404: Not Found
2025-03-14T06:17:01.5785533Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz ...
2025-03-14T06:17:01.6847914Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ...
2025-03-14T06:17:01.6932626Z Failed to download (trying next):
2025-03-14T06:17:01.6932969Z HTTP Error 404: Not Found
2025-03-14T06:17:01.6933483Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz ...
2025-03-14T06:17:01.7382175Z + [[ linux-focal-py3.13-clang10 == *asan* ]]
2025-03-14T06:17:01.7382635Z + [[ linux-focal-py3.13-clang10 == *slow-gradcheck* ]]
2025-03-14T06:17:01.7383232Z + OMP_NUM_THREADS=2
2025-03-14T06:17:01.7383635Z + TORCH_CPP_TEST_MNIST_PATH=/var/lib/jenkins/workspace/test/cpp/api/mnist
2025-03-14T06:17:01.7384254Z + python test/run_test.py --cpp --verbose -i cpp/test_api -k 'not IMethodTest'
2025-03-14T06:17:01.8403863Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-14T06:17:01.8404774Z   import pkg_resources
2025-03-14T06:17:05.5642939Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-14T06:17:05.5813613Z Found test times from artifacts
2025-03-14T06:17:05.6530784Z Found test times from artifacts
2025-03-14T06:17:05.6555529Z Running all tests
2025-03-14T06:17:05.6558743Z Running parallel tests on 3 processes
2025-03-14T06:17:05.6559535Z Name: tests to run (est. time: 0.0min)
2025-03-14T06:17:05.6559914Z   Serial tests (0):
2025-03-14T06:17:05.6560207Z   Parallel tests (1):
2025-03-14T06:17:05.6560484Z     cpp/test_api 1/1
2025-03-14T06:17:05.6560768Z Name: excluded (est. time: 0.0min)
2025-03-14T06:17:05.6561083Z   Serial tests (0):
2025-03-14T06:17:05.6561354Z   Parallel tests (0):
2025-03-14T06:17:05.6613645Z Running cpp/test_api 1/1 ... [2025-03-14 06:17:05.661095]
2025-03-14T06:17:05.6614082Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:17:05.6617436Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_api', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-23d6e60639ce6ab4.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2025-03-14 06:17:05.661483]
2025-03-14T06:17:07.9796170Z 
2025-03-14T06:17:07.9797729Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_9a707d4b8ae2b835_.log
2025-03-14T06:17:07.9799254Z 
2025-03-14T06:17:08.4467488Z Running cpp/test_api 1/1 ... [2025-03-14 06:17:08.446406]
2025-03-14T06:17:08.4468046Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:17:08.4470884Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_api', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-a285bd2c9c580a3e.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2025-03-14 06:17:08.446817]
2025-03-14T06:19:32.4247137Z 
2025-03-14T06:19:32.4247998Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_90ea10e60b29ad21_.log
2025-03-14T06:19:32.4260540Z 
2025-03-14T06:19:33.2374874Z Running test batch 'tests to run' cost 147.58 seconds
2025-03-14T06:19:33.9655048Z + [[ linux-focal-py3.13-clang10 != *s390x* ]]
2025-03-14T06:19:33.9655587Z + python test/run_test.py --cpp --verbose -i cpp/test_tensorexpr
2025-03-14T06:19:34.0681931Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-14T06:19:34.0682829Z   import pkg_resources
2025-03-14T06:19:37.7997531Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-14T06:19:37.8167627Z Found test times from artifacts
2025-03-14T06:19:37.8878598Z Found test times from artifacts
2025-03-14T06:19:37.8902754Z Running all tests
2025-03-14T06:19:37.8905972Z Running parallel tests on 3 processes
2025-03-14T06:19:37.8906732Z Name: tests to run (est. time: 0.0min)
2025-03-14T06:19:37.8907085Z   Serial tests (0):
2025-03-14T06:19:37.8907367Z   Parallel tests (1):
2025-03-14T06:19:37.8907665Z     cpp/test_tensorexpr 1/1
2025-03-14T06:19:37.8908001Z Name: excluded (est. time: 0.0min)
2025-03-14T06:19:37.8908329Z   Serial tests (0):
2025-03-14T06:19:37.8908605Z   Parallel tests (0):
2025-03-14T06:19:37.8958381Z Running cpp/test_tensorexpr 1/1 ... [2025-03-14 06:19:37.895549]
2025-03-14T06:19:37.8958875Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:19:37.8962050Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_tensorexpr', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1908180fc9876830.xml', '-x', '--reruns=2'] ... [2025-03-14 06:19:37.895949]
2025-03-14T06:19:39.7129460Z 
2025-03-14T06:19:39.7130883Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_ef9beb98793d2862_.log
2025-03-14T06:19:39.7132160Z 
2025-03-14T06:19:40.7537233Z Running cpp/test_tensorexpr 1/1 ... [2025-03-14 06:19:40.753338]
2025-03-14T06:19:40.7538020Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:19:40.7540724Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_tensorexpr', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-a0e85698fa3cffbf.xml', '-x', '--reruns=2'] ... [2025-03-14 06:19:40.753748]
2025-03-14T06:21:33.5960575Z 
2025-03-14T06:21:33.5963248Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_d07830f21e2f0840_.log
2025-03-14T06:21:33.5973224Z 
2025-03-14T06:21:34.4087178Z Running test batch 'tests to run' cost 116.52 seconds
2025-03-14T06:21:35.1463290Z + [[ linux-focal-py3.13-clang10 != *android* ]]
2025-03-14T06:21:35.1463735Z + [[ linux-focal-py3.13-clang10 != *cuda* ]]
2025-03-14T06:21:35.1464115Z + [[ linux-focal-py3.13-clang10 != *asan* ]]
2025-03-14T06:21:35.1464496Z + [[ linux-focal-py3.13-clang10 != *s390x* ]]
2025-03-14T06:21:35.1464859Z + export CPP_TESTS_DIR=build/bin
2025-03-14T06:21:35.1465184Z + CPP_TESTS_DIR=build/bin
2025-03-14T06:21:35.1465604Z + python test/run_test.py --cpp --verbose -i cpp/static_runtime_test
2025-03-14T06:21:35.2501770Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-14T06:21:35.2502736Z   import pkg_resources
2025-03-14T06:21:39.0120770Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-14T06:21:39.0291804Z Found test times from artifacts
2025-03-14T06:21:39.1004765Z Found test times from artifacts
2025-03-14T06:21:39.1029518Z Running all tests
2025-03-14T06:21:39.1032783Z Running parallel tests on 3 processes
2025-03-14T06:21:39.1033157Z Name: tests to run (est. time: 0.0min)
2025-03-14T06:21:39.1033506Z   Serial tests (0):
2025-03-14T06:21:39.1033779Z   Parallel tests (1):
2025-03-14T06:21:39.1034069Z     cpp/static_runtime_test 1/1
2025-03-14T06:21:39.1034397Z Name: excluded (est. time: 0.0min)
2025-03-14T06:21:39.1034712Z   Serial tests (0):
2025-03-14T06:21:39.1035000Z   Parallel tests (0):
2025-03-14T06:21:39.1084580Z Running cpp/static_runtime_test 1/1 ... [2025-03-14 06:21:39.108212]
2025-03-14T06:21:39.1085085Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:21:39.1088415Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2aa6221fd92e9a0b.xml', '-x', '--reruns=2'] ... [2025-03-14 06:21:39.108607]
2025-03-14T06:21:40.8757081Z 
2025-03-14T06:21:40.8758438Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_efabc3d72d034c81_.log
2025-03-14T06:21:40.8759668Z 
2025-03-14T06:21:41.9893759Z Running cpp/static_runtime_test 1/1 ... [2025-03-14 06:21:41.988987]
2025-03-14T06:21:41.9894664Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-14T06:21:41.9896602Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5886d6afa3d06f7a.xml', '-x', '--reruns=2'] ... [2025-03-14 06:21:41.989382]
2025-03-14T06:34:07.7666345Z 
2025-03-14T06:34:07.7667328Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_23651f1431a00af9_.log
2025-03-14T06:34:07.7671724Z 
2025-03-14T06:34:08.5705470Z Running test batch 'tests to run' cost 749.47 seconds
2025-03-14T06:34:09.2711538Z + [[ -z 1 ]]
2025-03-14T06:34:09.2711845Z + [[ 1 == \2 ]]
2025-03-14T06:34:09.2712109Z + assert_git_not_dirty
2025-03-14T06:34:09.2712430Z + [[ linux-focal-py3.13-clang10 != *rocm* ]]
2025-03-14T06:34:09.2712809Z + [[ linux-focal-py3.13-clang10 != *xla* ]]
2025-03-14T06:34:09.2717831Z ++ git status --porcelain
2025-03-14T06:34:09.2718225Z ++ grep -v '?? third_party'
2025-03-14T06:34:09.4977860Z ++ true
2025-03-14T06:34:09.4978466Z + git_status=
2025-03-14T06:34:09.4978789Z + [[ -n '' ]]
2025-03-14T06:34:09.4979448Z + [[ linux-focal-py3.13-clang10 == *xpu* ]]
2025-03-14T06:34:09.4980204Z + cleanup_workspace
2025-03-14T06:34:09.4980740Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.'
2025-03-14T06:34:09.4981768Z sudo may print the following warning message that can be ignored. The chown command will still run.
2025-03-14T06:34:09.4982431Z + echo '    sudo: setrlimit(RLIMIT_STACK): Operation not permitted'
2025-03-14T06:34:09.4982923Z     sudo: setrlimit(RLIMIT_STACK): Operation not permitted
2025-03-14T06:34:09.4983489Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42'
2025-03-14T06:34:09.4984096Z For more details refer to https://github.com/sudo-project/sudo/issues/42
2025-03-14T06:34:09.4984597Z + sudo chown -R 1000 /var/lib/jenkins/workspace
2025-03-14T06:34:12.4339086Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main
2025-03-14T06:34:12.4339598Z with:
2025-03-14T06:34:12.4339875Z   benchmark-results-dir: test/test-reports
2025-03-14T06:34:12.4340324Z   dry-run: false
2025-03-14T06:34:12.4340587Z   schema-version: v3
2025-03-14T06:34:12.4341082Z   github-token: ***
2025-03-14T06:34:12.4341339Z env:
2025-03-14T06:34:12.4341580Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:12.4342084Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:12.4342612Z ##[endgroup]
2025-03-14T06:34:12.4369304Z ##[group]Run set -eux
2025-03-14T06:34:12.4369615Z set -eux
2025-03-14T06:34:12.4369918Z python3 -mpip install boto3==1.35.33
2025-03-14T06:34:12.4378517Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:12.4378920Z env:
2025-03-14T06:34:12.4379166Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:12.4379712Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:12.4380226Z ##[endgroup]
2025-03-14T06:34:12.4407874Z + python3 -mpip install boto3==1.35.33
2025-03-14T06:34:12.6974694Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T06:34:13.7538524Z Collecting boto3==1.35.33
2025-03-14T06:34:13.7572581Z   Using cached boto3-1.35.33-py3-none-any.whl (139 kB)
2025-03-14T06:34:13.7635561Z Requirement already satisfied: botocore<1.36.0,>=1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (1.35.99)
2025-03-14T06:34:13.7639878Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0)
2025-03-14T06:34:13.7645650Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.4)
2025-03-14T06:34:13.7705237Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10)
2025-03-14T06:34:13.7712382Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/.local/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.2)
2025-03-14T06:34:13.7752128Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0)
2025-03-14T06:34:13.9387968Z Installing collected packages: boto3
2025-03-14T06:34:13.9388660Z   Attempting uninstall: boto3
2025-03-14T06:34:13.9390260Z     Found existing installation: boto3 1.35.42
2025-03-14T06:34:13.9492688Z     Uninstalling boto3-1.35.42:
2025-03-14T06:34:13.9511180Z       Successfully uninstalled boto3-1.35.42
2025-03-14T06:34:14.0084194Z Successfully installed boto3-1.35.33
2025-03-14T06:34:14.0846277Z ##[group]Run set -eux
2025-03-14T06:34:14.0846594Z set -eux
2025-03-14T06:34:14.0846849Z 
2025-03-14T06:34:14.0847137Z if [[ -z "${GITHUB_TOKEN}" ]]; then
2025-03-14T06:34:14.0847529Z   echo "Missing github-token input"
2025-03-14T06:34:14.0847892Z   exit 1
2025-03-14T06:34:14.0848152Z fi
2025-03-14T06:34:14.0854626Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:14.0855044Z env:
2025-03-14T06:34:14.0855277Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:14.0855781Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:14.0856641Z   GITHUB_TOKEN: ***
2025-03-14T06:34:14.0856897Z ##[endgroup]
2025-03-14T06:34:14.0897326Z + [[ -z *** ]]
2025-03-14T06:34:14.0950166Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main
2025-03-14T06:34:14.0950633Z with:
2025-03-14T06:34:14.0951013Z   github-token: ***
2025-03-14T06:34:14.0951269Z env:
2025-03-14T06:34:14.0951507Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:14.0952157Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:14.0952686Z ##[endgroup]
2025-03-14T06:34:14.0976808Z ##[group]Run set -eux
2025-03-14T06:34:14.0977113Z set -eux
2025-03-14T06:34:14.0977365Z 
2025-03-14T06:34:14.0977941Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-14T06:34:14.0983557Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:14.0983956Z env:
2025-03-14T06:34:14.0984182Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:14.0984679Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:14.0985384Z   GITHUB_TOKEN: ***
2025-03-14T06:34:14.0985627Z ##[endgroup]
2025-03-14T06:34:14.1008036Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 13849515290 i-01e2d8c57795e0761
2025-03-14T06:34:16.6747794Z setting job-id=38754598997
2025-03-14T06:34:16.6748357Z setting job-name=linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T06:34:16.6862614Z ##[group]Run set -eux
2025-03-14T06:34:16.6862932Z set -eux
2025-03-14T06:34:16.6863204Z 
2025-03-14T06:34:16.6863628Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \
2025-03-14T06:34:16.6864184Z   --schema-version "${SCHEMA_VERSION}" \
2025-03-14T06:34:16.6864566Z   --repo "${REPO}" \
2025-03-14T06:34:16.6864897Z   --head-branch "${HEAD_BRANCH}" \
2025-03-14T06:34:16.6865264Z   --head-sha "${HEAD_SHA}" \
2025-03-14T06:34:16.6865635Z   --workflow-id "${WORKFLOW_RUN_ID}" \
2025-03-14T06:34:16.6866032Z   --run-attempt "${RUN_ATTEMPT}" \
2025-03-14T06:34:16.6866396Z   --job-id "${JOB_ID}" \
2025-03-14T06:34:16.6866729Z   --job-name "${JOB_NAME}"
2025-03-14T06:34:16.6872614Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:16.6873037Z env:
2025-03-14T06:34:16.6873288Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:16.6873787Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:16.6874432Z   SCHEMA_VERSION: v3
2025-03-14T06:34:16.6874713Z   REPO: pytorch/pytorch
2025-03-14T06:34:16.6875004Z   HEAD_BRANCH: refs/heads/main
2025-03-14T06:34:16.6875353Z   HEAD_SHA: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T06:34:16.6875725Z   WORKFLOW_RUN_ID: 13849515290
2025-03-14T06:34:16.6876018Z   RUN_ATTEMPT: 1
2025-03-14T06:34:16.6876269Z   JOB_ID: 38754598997
2025-03-14T06:34:16.6876676Z   JOB_NAME: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T06:34:16.6877148Z ##[endgroup]
2025-03-14T06:34:16.6902266Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/main --head-sha aed0b7a742a2d7b7901790622829cbd2135049a4 --workflow-id 13849515290 --run-attempt 1 --job-id 38754598997 --job-name 'linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)'
2025-03-14T06:34:16.7222581Z ##[group]Run set -eux
2025-03-14T06:34:16.7222883Z set -eux
2025-03-14T06:34:16.7223138Z 
2025-03-14T06:34:16.7223415Z # TODO (huydhn): Implement this part
2025-03-14T06:34:16.7223819Z echo "runners=[]" >> "${GITHUB_OUTPUT}"
2025-03-14T06:34:16.7233006Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:16.7233420Z env:
2025-03-14T06:34:16.7233657Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:16.7234157Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:16.7234689Z ##[endgroup]
2025-03-14T06:34:16.7257540Z + echo 'runners=[]'
2025-03-14T06:34:16.7285311Z ##[group]Run set -eux
2025-03-14T06:34:16.7285605Z set -eux
2025-03-14T06:34:16.7285863Z 
2025-03-14T06:34:16.7286284Z # TODO (huydhn): Implement this part
2025-03-14T06:34:16.7286704Z echo "dependencies={}" >> "${GITHUB_OUTPUT}"
2025-03-14T06:34:16.7292245Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:16.7292715Z env:
2025-03-14T06:34:16.7292962Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:16.7293458Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:16.7293991Z ##[endgroup]
2025-03-14T06:34:16.7315465Z + echo 'dependencies={}'
2025-03-14T06:34:16.7343721Z ##[group]Run set -eux
2025-03-14T06:34:16.7344028Z set -eux
2025-03-14T06:34:16.7344282Z 
2025-03-14T06:34:16.7344606Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then
2025-03-14T06:34:16.7345074Z   echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping"
2025-03-14T06:34:16.7345593Z   # We don't want the job to fail if the directory doesn't exist
2025-03-14T06:34:16.7346013Z   exit 0
2025-03-14T06:34:16.7346279Z fi
2025-03-14T06:34:16.7346517Z 
2025-03-14T06:34:16.7346777Z if [[ "${DRY_RUN}" == "true" ]]; then
2025-03-14T06:34:16.7347291Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-14T06:34:16.7347888Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-14T06:34:16.7348349Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-14T06:34:16.7348926Z     --runners "${RUNNER_INFO}" \
2025-03-14T06:34:16.7349335Z     --dependencies "${DEPENDENCIES}" \
2025-03-14T06:34:16.7349694Z     --dry-run
2025-03-14T06:34:16.7349970Z else
2025-03-14T06:34:16.7350368Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-14T06:34:16.7350950Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-14T06:34:16.7351408Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-14T06:34:16.7351799Z     --runners "${RUNNER_INFO}" \
2025-03-14T06:34:16.7352178Z     --dependencies "${DEPENDENCIES}"
2025-03-14T06:34:16.7352612Z fi
2025-03-14T06:34:16.7357762Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:16.7358169Z env:
2025-03-14T06:34:16.7358415Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:16.7358911Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:16.7359549Z   BENCHMARK_RESULTS_DIR: test/test-reports
2025-03-14T06:34:16.7359903Z   DRY_RUN: false
2025-03-14T06:34:16.7361203Z   BENCHMARK_METADATA: {"timestamp": 1741934056, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "aed0b7a742a2d7b7901790622829cbd2135049a4", "workflow_id": 13849515290, "run_attempt": 1, "job_id": 38754598997}
2025-03-14T06:34:16.7362571Z   RUNNER_INFO: []
2025-03-14T06:34:16.7362841Z   DEPENDENCIES: {}
2025-03-14T06:34:16.7363100Z ##[endgroup]
2025-03-14T06:34:16.7383531Z + [[ ! -d test/test-reports ]]
2025-03-14T06:34:16.7383872Z + [[ false == \t\r\u\e ]]
2025-03-14T06:34:16.7386252Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1741934056, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "aed0b7a742a2d7b7901790622829cbd2135049a4", "workflow_id": 13849515290, "run_attempt": 1, "job_id": 38754598997}' --runners '[]' --dependencies '{}'
2025-03-14T06:34:16.9127212Z ##[group]Run cat test/**/*_toprint.log || true
2025-03-14T06:34:16.9127631Z cat test/**/*_toprint.log || true
2025-03-14T06:34:16.9133364Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:16.9133784Z env:
2025-03-14T06:34:16.9134042Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:16.9134540Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:16.9135164Z ##[endgroup]
2025-03-14T06:34:16.9206805Z cat: 'test/**/*_toprint.log': No such file or directory
2025-03-14T06:34:16.9243162Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2025-03-14T06:34:16.9243545Z kill "$MONITOR_SCRIPT_PID"
2025-03-14T06:34:16.9249388Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:16.9249806Z env:
2025-03-14T06:34:16.9250054Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:16.9250540Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:16.9251082Z   MONITOR_SCRIPT_PID: 66041
2025-03-14T06:34:16.9251376Z ##[endgroup]
2025-03-14T06:34:16.9405434Z Prepare all required actions
2025-03-14T06:34:16.9405908Z Getting action download info
2025-03-14T06:34:17.0519449Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1)
2025-03-14T06:34:17.5873025Z ##[group]Run ./.github/actions/upload-test-artifacts
2025-03-14T06:34:17.5873433Z with:
2025-03-14T06:34:17.5873773Z   file-suffix: test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T06:34:17.5874199Z   s3-bucket: gha-artifacts
2025-03-14T06:34:17.5874479Z env:
2025-03-14T06:34:17.5874715Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:17.5875203Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:17.5875731Z ##[endgroup]
2025-03-14T06:34:17.5905033Z ##[group]Run # Remove any previous test jsons if they exist
2025-03-14T06:34:17.5905530Z # Remove any previous test jsons if they exist
2025-03-14T06:34:17.5905938Z rm -f test-jsons-*.zip
2025-03-14T06:34:17.5906444Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json'
2025-03-14T06:34:17.5912190Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:17.5912590Z env:
2025-03-14T06:34:17.5912845Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:17.5913437Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:17.5914068Z   FILE_SUFFIX: test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T06:34:17.5914473Z ##[endgroup]
2025-03-14T06:34:17.5986713Z   adding: test/test-reports/td_exclusions-20bdac4ec0c1f64cba9f.json (deflated 16%)
2025-03-14T06:34:17.5990343Z   adding: test/test-reports/td_exclusions-f2e7b14e235f94084008.json (deflated 81%)
2025-03-14T06:34:17.5991025Z   adding: test/test-reports/td_exclusions-b19752a9ebb86f8e379d.json (deflated 73%)
2025-03-14T06:34:17.5991699Z   adding: test/test-reports/td_exclusions-04b1ce3b21caa2adc340.json (deflated 18%)
2025-03-14T06:34:17.5992390Z   adding: test/test-reports/td_exclusions-0a8593859e0ae0c724aa.json (deflated 16%)
2025-03-14T06:34:17.5993059Z   adding: test/test-reports/td_exclusions-fa08e831ec8032af86f7.json (deflated 14%)
2025-03-14T06:34:17.6018432Z ##[group]Run # Remove any previous test reports if they exist
2025-03-14T06:34:17.6018937Z # Remove any previous test reports if they exist
2025-03-14T06:34:17.6019370Z rm -f test-reports-*.zip
2025-03-14T06:34:17.6019875Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv'
2025-03-14T06:34:17.6025450Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:17.6025853Z env:
2025-03-14T06:34:17.6026106Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:17.6026606Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:17.6027229Z   FILE_SUFFIX: test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T06:34:17.6027634Z ##[endgroup]
2025-03-14T06:34:17.6119312Z   adding: test/test-reports/python-pytest/lazy.test_ts_opinfo/lazy.test_ts_opinfo-9a69b976ecadf787.xml (deflated 28%)
2025-03-14T06:34:17.6122565Z   adding: test/test-reports/python-pytest/lazy.test_ts_opinfo/lazy.test_ts_opinfo-d5f5cf92c7cef291.xml (deflated 94%)
2025-03-14T06:34:17.6137030Z   adding: test/test-reports/python-pytest/inductor.test_flex_attention/inductor.test_flex_attention-e80d6953514bc5c1.xml (deflated 98%)
2025-03-14T06:34:17.6184105Z   adding: test/test-reports/python-pytest/test_reductions/test_reductions-8069d11811a20723.xml (deflated 96%)
2025-03-14T06:34:17.6197861Z   adding: test/test-reports/python-pytest/test_overrides/test_overrides-aa5660f60c143605.xml (deflated 95%)
2025-03-14T06:34:17.6198886Z   adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-aa0cb68e519812f9.xml (deflated 85%)
2025-03-14T06:34:17.6200295Z   adding: test/test-reports/python-pytest/inductor.test_distributed_patterns/inductor.test_distributed_patterns-5b476be41d53f485.xml (deflated 86%)
2025-03-14T06:34:17.6208215Z   adding: test/test-reports/python-pytest/test_cpp_api_parity/test_cpp_api_parity-b34b911bd701cda3.xml (deflated 96%)
2025-03-14T06:34:17.6209325Z   adding: test/test-reports/python-pytest/inductor.test_benchmark_fusion/inductor.test_benchmark_fusion-bc02a331b9a8b823.xml (deflated 71%)
2025-03-14T06:34:17.6210427Z   adding: test/test-reports/python-pytest/test_extension_utils/test_extension_utils-1e205b3f2ce172e2.xml (deflated 52%)
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2025-03-14T06:34:17.7485020Z   adding: test/test-reports/python-pytest/test_sparse/test_sparse-6df69e3f33f9001a.xml (deflated 96%)
2025-03-14T06:34:17.7486018Z   adding: test/test-reports/python-pytest/functorch.test_aotdispatch/functorch.test_aotdispatch-c50a2e814435b980.xml (deflated 28%)
2025-03-14T06:34:17.7498269Z   adding: test/test-reports/python-pytest/functorch.test_aotdispatch/functorch.test_aotdispatch-da46ec14b6de2072.xml (deflated 92%)
2025-03-14T06:34:17.7499326Z   adding: test/test-reports/python-pytest/test_quantization/test_quantization-798887416fc34034.xml (deflated 28%)
2025-03-14T06:34:17.7635776Z   adding: test/test-reports/python-pytest/test_quantization/test_quantization-53d76407096b5310.xml (deflated 98%)
2025-03-14T06:34:17.7636716Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-665d279c84192f5f.xml (deflated 29%)
2025-03-14T06:34:17.7637709Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-3233300dfa87549a.xml (deflated 29%)
2025-03-14T06:34:17.7638580Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-d2711fefcdd6dbc8.xml (deflated 29%)
2025-03-14T06:34:17.7639530Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-794a8d132f3b2a94.xml (deflated 29%)
2025-03-14T06:34:17.7640402Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-d99666ffec3857cd.xml (deflated 29%)
2025-03-14T06:34:17.7641269Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-fa115f71b92bf942.xml (deflated 29%)
2025-03-14T06:34:17.7642130Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-d64723b723efb873.xml (deflated 29%)
2025-03-14T06:34:17.7642992Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-f2bd59b4ee771128.xml (deflated 29%)
2025-03-14T06:34:17.7643856Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-62377b90928dc283.xml (deflated 28%)
2025-03-14T06:34:17.7644731Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-dcc57e04775dece2.xml (deflated 29%)
2025-03-14T06:34:17.7645600Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-d692ec6cc0db25bb.xml (deflated 29%)
2025-03-14T06:34:17.7646470Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-37e03d09ac4be116.xml (deflated 29%)
2025-03-14T06:34:17.7647391Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-50f97b6e01a6a1cf.xml (deflated 29%)
2025-03-14T06:34:17.7648275Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-e2f11ff3ef38fd20.xml (deflated 29%)
2025-03-14T06:34:17.7649524Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-0d8f5781e4e70b70.xml (deflated 29%)
2025-03-14T06:34:17.7650394Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-9838bcf9a7736b17.xml (deflated 29%)
2025-03-14T06:34:17.7651267Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-5a4c12baf474bbcc.xml (deflated 29%)
2025-03-14T06:34:17.7652135Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-718bf63d8bf14548.xml (deflated 29%)
2025-03-14T06:34:17.7652995Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-5e10f60cbf45c7a0.xml (deflated 29%)
2025-03-14T06:34:17.7653856Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-4765f48af6833717.xml (deflated 57%)
2025-03-14T06:34:17.7654720Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1ba53aada6a8e8d4.xml (deflated 73%)
2025-03-14T06:34:17.7655587Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-e4b6e160c79af61f.xml (deflated 84%)
2025-03-14T06:34:17.7656455Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-bb7bc73b213bb3b0.xml (deflated 67%)
2025-03-14T06:34:17.7657322Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-98d34401da3c8a6d.xml (deflated 79%)
2025-03-14T06:34:17.7658189Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-2c0874781dcf62d6.xml (deflated 61%)
2025-03-14T06:34:17.7659050Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-a0d16688c4d5f6e8.xml (deflated 37%)
2025-03-14T06:34:17.7659915Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-26ce1ebea7d1631a.xml (deflated 81%)
2025-03-14T06:34:17.7660844Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1d864d978782b5f6.xml (deflated 50%)
2025-03-14T06:34:17.7661706Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-10715263599f8749.xml (deflated 35%)
2025-03-14T06:34:17.7662568Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-3f245f9f934592aa.xml (deflated 47%)
2025-03-14T06:34:17.7663437Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-dd91ae732b2045fd.xml (deflated 47%)
2025-03-14T06:34:17.7664388Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-d2ce491c33ca3eb2.xml (deflated 84%)
2025-03-14T06:34:17.7665257Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-9ed7749f4f74efe5.xml (deflated 58%)
2025-03-14T06:34:17.7666123Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1e9c6ed13d791e8a.xml (deflated 58%)
2025-03-14T06:34:17.7666993Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-216f4e86d0cc8ff1.xml (deflated 60%)
2025-03-14T06:34:17.7667868Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-0df7203c55c43acf.xml (deflated 37%)
2025-03-14T06:34:17.7668731Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-150dcc55b242ab63.xml (deflated 36%)
2025-03-14T06:34:17.7669589Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-735e79d733db78e4.xml (deflated 91%)
2025-03-14T06:34:17.7670452Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-23d6e60639ce6ab4.xml (deflated 28%)
2025-03-14T06:34:17.7671323Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-a285bd2c9c580a3e.xml (deflated 88%)
2025-03-14T06:34:17.7672182Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1908180fc9876830.xml (deflated 28%)
2025-03-14T06:34:17.7675887Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-a0e85698fa3cffbf.xml (deflated 89%)
2025-03-14T06:34:17.7676813Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-2aa6221fd92e9a0b.xml (deflated 29%)
2025-03-14T06:34:17.7681648Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-5886d6afa3d06f7a.xml (deflated 90%)
2025-03-14T06:34:17.7682632Z   adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20250314043310.xml (deflated 43%)
2025-03-14T06:34:17.7683721Z   adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20250314043334.xml (deflated 43%)
2025-03-14T06:34:17.7729084Z ##[group]Run # Remove any previous usage logs if they exist
2025-03-14T06:34:17.7729601Z # Remove any previous usage logs if they exist
2025-03-14T06:34:17.7730026Z rm -f logs-*.zip
2025-03-14T06:34:17.7730635Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2025-03-14T06:34:17.7731211Z # so check to see if the file exists first
2025-03-14T06:34:17.7731613Z if [ -f 'usage_log.txt' ]; then
2025-03-14T06:34:17.7732010Z     zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt'
2025-03-14T06:34:17.7732381Z fi
2025-03-14T06:34:17.7732778Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then
2025-03-14T06:34:17.7733366Z     zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log'
2025-03-14T06:34:17.7733785Z fi
2025-03-14T06:34:17.7739367Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:17.7739768Z env:
2025-03-14T06:34:17.7740015Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:17.7740524Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:17.7741221Z   FILE_SUFFIX: test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T06:34:17.7741636Z ##[endgroup]
2025-03-14T06:34:17.7843911Z   adding: usage_log.txt (deflated 96%)
2025-03-14T06:34:17.7922632Z ##[group]Run # Remove any previous debugging artifacts if they exist
2025-03-14T06:34:17.7923258Z # Remove any previous debugging artifacts if they exist
2025-03-14T06:34:17.7923686Z rm -f debug-*.zip
2025-03-14T06:34:17.7923998Z if [ -d 'test/debug' ]; then
2025-03-14T06:34:17.7924386Z   zip -r "debug-${FILE_SUFFIX}.zip" test/debug
2025-03-14T06:34:17.7924758Z fi
2025-03-14T06:34:17.7930045Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:17.7930452Z env:
2025-03-14T06:34:17.7930709Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:17.7931353Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:17.7931982Z   FILE_SUFFIX: test-default-1-5-lf.linux.4xlarge_38754598997
2025-03-14T06:34:17.7932386Z ##[endgroup]
2025-03-14T06:34:17.8010993Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T06:34:17.8011355Z with:
2025-03-14T06:34:17.8011607Z   s3-bucket: gha-artifacts
2025-03-14T06:34:17.8011970Z   s3-prefix: pytorch/pytorch/13849515290/1/artifact

2025-03-14T06:34:17.8012350Z   retention-days: 14
2025-03-14T06:34:17.8012626Z   if-no-files-found: warn
2025-03-14T06:34:17.8012921Z   path: test-jsons-*.zip
2025-03-14T06:34:17.8013202Z   name: artifact
2025-03-14T06:34:17.8013454Z   region: us-east-1
2025-03-14T06:34:17.8013700Z env:
2025-03-14T06:34:17.8013932Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:17.8014427Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:17.8014946Z ##[endgroup]
2025-03-14T06:34:18.1627451Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T06:34:18.1628028Z With the provided path, there will be 1 file uploaded
2025-03-14T06:34:18.1628513Z Uploading to s3 prefix: pytorch/pytorch/13849515290/1/artifact
2025-03-14T06:34:18.1668193Z Starting upload of test-jsons-test-default-1-5-lf.linux.4xlarge_38754598997.zip
2025-03-14T06:34:18.3344516Z Finished upload of test-jsons-test-default-1-5-lf.linux.4xlarge_38754598997.zip
2025-03-14T06:34:18.3535622Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T06:34:18.3536011Z with:
2025-03-14T06:34:18.3536272Z   s3-bucket: gha-artifacts
2025-03-14T06:34:18.3536628Z   s3-prefix: pytorch/pytorch/13849515290/1/artifact

2025-03-14T06:34:18.3537009Z   retention-days: 14
2025-03-14T06:34:18.3537298Z   if-no-files-found: error
2025-03-14T06:34:18.3537604Z   path: test-reports-*.zip
2025-03-14T06:34:18.3537897Z   name: artifact
2025-03-14T06:34:18.3538157Z   region: us-east-1
2025-03-14T06:34:18.3538402Z env:
2025-03-14T06:34:18.3538645Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:18.3539148Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:18.3539671Z ##[endgroup]
2025-03-14T06:34:18.6858125Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T06:34:18.6858660Z With the provided path, there will be 1 file uploaded
2025-03-14T06:34:18.6859144Z Uploading to s3 prefix: pytorch/pytorch/13849515290/1/artifact
2025-03-14T06:34:18.6898471Z Starting upload of test-reports-test-default-1-5-lf.linux.4xlarge_38754598997.zip
2025-03-14T06:34:18.8960396Z Finished upload of test-reports-test-default-1-5-lf.linux.4xlarge_38754598997.zip
2025-03-14T06:34:18.9155835Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T06:34:18.9156203Z with:
2025-03-14T06:34:18.9156455Z   s3-bucket: gha-artifacts
2025-03-14T06:34:18.9156801Z   s3-prefix: pytorch/pytorch/13849515290/1/artifact

2025-03-14T06:34:18.9157176Z   retention-days: 14
2025-03-14T06:34:18.9157458Z   if-no-files-found: ignore
2025-03-14T06:34:18.9157755Z   path: logs-*.zip
2025-03-14T06:34:18.9158015Z   name: artifact
2025-03-14T06:34:18.9158274Z   region: us-east-1
2025-03-14T06:34:18.9158528Z env:
2025-03-14T06:34:18.9158770Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:18.9159361Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:18.9159892Z ##[endgroup]
2025-03-14T06:34:19.2466918Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T06:34:19.2467657Z With the provided path, there will be 1 file uploaded
2025-03-14T06:34:19.2468136Z Uploading to s3 prefix: pytorch/pytorch/13849515290/1/artifact
2025-03-14T06:34:19.2506848Z Starting upload of logs-test-default-1-5-lf.linux.4xlarge_38754598997.zip
2025-03-14T06:34:19.4217943Z Finished upload of logs-test-default-1-5-lf.linux.4xlarge_38754598997.zip
2025-03-14T06:34:19.4406647Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T06:34:19.4407013Z with:
2025-03-14T06:34:19.4407267Z   s3-bucket: gha-artifacts
2025-03-14T06:34:19.4407615Z   s3-prefix: pytorch/pytorch/13849515290/1/artifact

2025-03-14T06:34:19.4407996Z   retention-days: 14
2025-03-14T06:34:19.4408421Z   if-no-files-found: ignore
2025-03-14T06:34:19.4408730Z   path: debug-*.zip
2025-03-14T06:34:19.4408994Z   name: artifact
2025-03-14T06:34:19.4409236Z   region: us-east-1
2025-03-14T06:34:19.4409489Z env:
2025-03-14T06:34:19.4409735Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:19.4410243Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:19.4410781Z ##[endgroup]
2025-03-14T06:34:19.7647627Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded.
2025-03-14T06:34:19.7840879Z ##[group]Run # shellcheck disable=SC2156
2025-03-14T06:34:19.7841292Z # shellcheck disable=SC2156
2025-03-14T06:34:19.7841904Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2025-03-14T06:34:19.7847979Z shell: /usr/bin/bash -e {0}
2025-03-14T06:34:19.7848285Z env:
2025-03-14T06:34:19.7848521Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:19.7849209Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:19.7849746Z ##[endgroup]
2025-03-14T06:34:20.0305933Z Prepare all required actions
2025-03-14T06:34:20.0306367Z Getting action download info
2025-03-14T06:34:20.1477045Z ##[group]Run ./.github/actions/upload-utilization-stats
2025-03-14T06:34:20.1477557Z with:
2025-03-14T06:34:20.1477792Z   job_id: 38754598997
2025-03-14T06:34:20.1478222Z   job_name: linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T06:34:20.1478710Z   workflow_name: pull
2025-03-14T06:34:20.1478995Z   workflow_run_id: 13849515290
2025-03-14T06:34:20.1479384Z   workflow_attempt: 1
2025-03-14T06:34:20.1479653Z env:
2025-03-14T06:34:20.1479900Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:20.1480399Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:20.1480931Z ##[endgroup]
2025-03-14T06:34:20.1501060Z ##[group]Run echo "workflow_id: 13849515290"
2025-03-14T06:34:20.1501457Z echo "workflow_id: 13849515290"
2025-03-14T06:34:20.1501813Z echo "workflow_attempt: 1"
2025-03-14T06:34:20.1502213Z echo "workflow_Name: pull"
2025-03-14T06:34:20.1502547Z echo "job_id: 38754598997"
2025-03-14T06:34:20.1503072Z echo "job_name:  linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)"
2025-03-14T06:34:20.1509108Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:20.1509514Z env:
2025-03-14T06:34:20.1509762Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:20.1510262Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:20.1510791Z ##[endgroup]
2025-03-14T06:34:20.1533245Z workflow_id: 13849515290
2025-03-14T06:34:20.1533793Z workflow_attempt: 1
2025-03-14T06:34:20.1534154Z workflow_Name: pull
2025-03-14T06:34:20.1534569Z job_id: 38754598997
2025-03-14T06:34:20.1534996Z job_name:  linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)
2025-03-14T06:34:20.1573467Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T06:34:20.1573794Z with:
2025-03-14T06:34:20.1574034Z   shell: bash
2025-03-14T06:34:20.1574283Z   timeout_minutes: 5
2025-03-14T06:34:20.1574551Z   max_attempts: 5
2025-03-14T06:34:20.1574813Z   retry_wait_seconds: 30
2025-03-14T06:34:20.1575303Z   command: set -eu
python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3

2025-03-14T06:34:20.1575913Z   polling_interval_seconds: 1
2025-03-14T06:34:20.1576220Z   warning_on_retry: true
2025-03-14T06:34:20.1576501Z   continue_on_error: false
2025-03-14T06:34:20.1576773Z env:
2025-03-14T06:34:20.1577001Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:20.1577485Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:20.1578003Z ##[endgroup]
2025-03-14T06:34:20.4874998Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T06:34:20.5045388Z Requirement already satisfied: python-dateutil==2.8.2 in /home/ec2-user/.local/lib/python3.9/site-packages (2.8.2)
2025-03-14T06:34:21.4896104Z Collecting boto3==1.35.42
2025-03-14T06:34:21.4931334Z   Using cached boto3-1.35.42-py3-none-any.whl (139 kB)
2025-03-14T06:34:21.4957495Z Requirement already satisfied: pandas==2.1.3 in /home/ec2-user/.local/lib/python3.9/site-packages (2.1.3)
2025-03-14T06:34:21.4973212Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0)
2025-03-14T06:34:21.5018908Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0)
2025-03-14T06:34:21.5022936Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4)
2025-03-14T06:34:21.5027047Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99)
2025-03-14T06:34:21.5906222Z Requirement already satisfied: numpy<2,>=1.22.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from pandas==2.1.3) (1.26.4)
2025-03-14T06:34:21.5910512Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1)
2025-03-14T06:34:21.5917411Z Requirement already satisfied: tzdata>=2022.1 in /home/ec2-user/.local/lib/python3.9/site-packages (from pandas==2.1.3) (2025.1)
2025-03-14T06:34:21.5975045Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10)
2025-03-14T06:34:21.6796074Z Installing collected packages: boto3
2025-03-14T06:34:21.6796457Z   Attempting uninstall: boto3
2025-03-14T06:34:21.6800181Z     Found existing installation: boto3 1.35.33
2025-03-14T06:34:21.6896988Z     Uninstalling boto3-1.35.33:
2025-03-14T06:34:21.6909369Z       Successfully uninstalled boto3-1.35.33
2025-03-14T06:34:21.7455884Z Successfully installed boto3-1.35.42
2025-03-14T06:34:22.2342946Z Command completed after 1 attempt(s).
2025-03-14T06:34:22.2399704Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-14T06:34:22.2400439Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-14T06:34:22.2400993Z   --workflow-run-id "13849515290" \
2025-03-14T06:34:22.2401372Z   --workflow-name "pull" \
2025-03-14T06:34:22.2401724Z   --workflow-run-attempt "1" \
2025-03-14T06:34:22.2402075Z   --job-id "38754598997" \
2025-03-14T06:34:22.2402571Z   --job-name "linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)"
2025-03-14T06:34:22.2408471Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:22.2408875Z env:
2025-03-14T06:34:22.2409124Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:22.2409623Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:22.2410152Z ##[endgroup]
2025-03-14T06:34:24.5439591Z repo: pytorch/pytorch
2025-03-14T06:34:24.5440138Z Downloading logs-test-default-1-5-lf.linux.4xlarge_38754598997.zip
2025-03-14T06:34:24.5440638Z Converted Log Model: UtilizationMetadata:
2025-03-14T06:34:24.5441998Z  UtilizationMetadata(level='metadata', workflow_id='13849515290', job_id='38754598997', workflow_name='pull', job_name='linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741925223, gpu_count=0, cpu_count=16, gpu_type='', error=None)
2025-03-14T06:34:24.5443693Z [Db Segments] detected pytest cmd: 16, generated segments: 16
2025-03-14T06:34:24.5444124Z [db model] Peek db timeseries 
2025-03-14T06:34:24.5444422Z :{
2025-03-14T06:34:24.5444653Z     "created_at": 1741934064,
2025-03-14T06:34:24.5444960Z     "type": "utilization",
2025-03-14T06:34:24.5445242Z     "tags": [
2025-03-14T06:34:24.5445477Z         "record"
2025-03-14T06:34:24.5445719Z     ],
2025-03-14T06:34:24.5445955Z     "time_stamp": 1741925223,
2025-03-14T06:34:24.5446261Z     "repo": "pytorch/pytorch",
2025-03-14T06:34:24.5446568Z     "workflow_id": 13849515290,
2025-03-14T06:34:24.5446874Z     "run_attempt": 1,
2025-03-14T06:34:24.5447144Z     "job_id": 38754598997,
2025-03-14T06:34:24.5447433Z     "workflow_name": "pull",
2025-03-14T06:34:24.5447877Z     "job_name": "linux-focal-py3.13-clang10 / test (default, 1, 5, lf.linux.4xlarge)",
2025-03-14T06:34:24.5448367Z     "json_data": "{}"
2025-03-14T06:34:24.5448622Z }
2025-03-14T06:34:24.5449517Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13849515290/1/38754598997/metadata
2025-03-14T06:34:24.5450474Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13849515290/1/38754598997/metadata 
2025-03-14T06:34:24.5451464Z Writing 1765 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13849515290/1/38754598997/time_series
2025-03-14T06:34:24.5452473Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13849515290/1/38754598997/time_series 
2025-03-14T06:34:24.6540396Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2025-03-14T06:34:24.6540864Z with:
2025-03-14T06:34:24.6541093Z env:
2025-03-14T06:34:24.6541339Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:24.6541937Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:24.6542479Z ##[endgroup]
2025-03-14T06:34:24.6567124Z ##[group]Run set -eou pipefail
2025-03-14T06:34:24.6567492Z set -eou pipefail
2025-03-14T06:34:24.6567793Z 
2025-03-14T06:34:24.6568195Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2025-03-14T06:34:24.6568695Z for _ in $(seq 1440); do
2025-03-14T06:34:24.6569062Z     # Break if no ssh session exists anymore
2025-03-14T06:34:24.6569442Z     if [ "$(who)" = "" ]; then
2025-03-14T06:34:24.6569770Z       break
2025-03-14T06:34:24.6570020Z     fi
2025-03-14T06:34:24.6570332Z     echo "."
2025-03-14T06:34:24.6570603Z     sleep 5
2025-03-14T06:34:24.6570876Z done
2025-03-14T06:34:24.6576562Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:24.6576980Z env:
2025-03-14T06:34:24.6577233Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:24.6577746Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:24.6578280Z ##[endgroup]
2025-03-14T06:34:24.6601184Z Holding runner for 2 hours until all ssh sessions have logged out
2025-03-14T06:34:24.6665763Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T06:34:24.6666363Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T06:34:24.6666823Z # shellcheck disable=SC2046
2025-03-14T06:34:24.6667191Z docker stop $(docker ps -q) || true
2025-03-14T06:34:24.6667564Z # Prune all of the docker images
2025-03-14T06:34:24.6667918Z docker system prune -af
2025-03-14T06:34:24.6673372Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:24.6673779Z env:
2025-03-14T06:34:24.6674026Z   GIT_DEFAULT_BRANCH: main
2025-03-14T06:34:24.6674523Z   DOCKER_CONTAINER_ID: a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:24.6675163Z ##[endgroup]
2025-03-14T06:34:25.3815235Z a62caa0dfbc6
2025-03-14T06:34:26.6866536Z Deleted Containers:
2025-03-14T06:34:26.6867037Z a62caa0dfbc6aa05ba5792c32be66086180248e7a481d59d70eff2cf107663ad
2025-03-14T06:34:26.6867397Z 
2025-03-14T06:34:29.7602031Z Deleted Images:
2025-03-14T06:34:29.7602901Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T06:34:29.7604675Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10@sha256:f3d6a3edfcad1af24cc8a256a5d3fd25be79458006d512d917d0ff8916c757ad
2025-03-14T06:34:29.7605785Z deleted: sha256:15123f02dc5fec3ea10360b1638dd75185500be8f3066081ab6efeae68fe77ab
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2025-03-14T06:34:29.7768608Z Post job cleanup.
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2025-03-14T06:34:29.8845675Z [command]/usr/bin/git version
2025-03-14T06:34:29.8896755Z git version 2.47.1
2025-03-14T06:34:29.8930958Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/b57c88f0-bcd7-4d2c-a8a0-ee7ce2cdc514/.gitconfig'
2025-03-14T06:34:29.8943427Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/b57c88f0-bcd7-4d2c-a8a0-ee7ce2cdc514' before making global git config changes
2025-03-14T06:34:29.8944411Z Adding repository directory to the temporary git global config as a safe directory
2025-03-14T06:34:29.8948420Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T06:34:29.8981978Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-14T06:34:29.9015751Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
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2025-03-14T06:34:30.3153923Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-14T06:34:30.3172881Z http.https://github.com/.extraheader
2025-03-14T06:34:30.3182935Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2025-03-14T06:34:30.3212638Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
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2025-03-14T06:34:30.6364558Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6397406Z Entering 'third_party/kleidiai'
2025-03-14T06:34:30.6433350Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6464863Z Entering 'third_party/mimalloc'
2025-03-14T06:34:30.6499195Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6529673Z Entering 'third_party/nlohmann'
2025-03-14T06:34:30.6563833Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6594529Z Entering 'third_party/onnx'
2025-03-14T06:34:30.6628647Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6677996Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T06:34:30.6712839Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6746905Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T06:34:30.6782273Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6815561Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T06:34:30.6850174Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6881370Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T06:34:30.6915781Z http.https://github.com/.extraheader
2025-03-14T06:34:30.6946979Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T06:34:30.6981158Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7011577Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T06:34:30.7045240Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7078298Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T06:34:30.7111497Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7141988Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T06:34:30.7175659Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7205725Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T06:34:30.7238987Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7269113Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T06:34:30.7302352Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7334473Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T06:34:30.7367842Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7398525Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T06:34:30.7432083Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7484372Z Entering 'third_party/pocketfft'
2025-03-14T06:34:30.7519324Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7549451Z Entering 'third_party/protobuf'
2025-03-14T06:34:30.7583822Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7617841Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T06:34:30.7652119Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7681483Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T06:34:30.7715316Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7748599Z Entering 'third_party/psimd'
2025-03-14T06:34:30.7784008Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7813919Z Entering 'third_party/pthreadpool'
2025-03-14T06:34:30.7847407Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7877162Z Entering 'third_party/pybind11'
2025-03-14T06:34:30.7911423Z http.https://github.com/.extraheader
2025-03-14T06:34:30.7941722Z Entering 'third_party/python-peachpy'
2025-03-14T06:34:30.7975668Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8004940Z Entering 'third_party/sleef'
2025-03-14T06:34:30.8038141Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8068641Z Entering 'third_party/tensorpipe'
2025-03-14T06:34:30.8102077Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8131946Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T06:34:30.8164868Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8194000Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T06:34:30.8226378Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8257392Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T06:34:30.8290896Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8320848Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T06:34:30.8353341Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8382353Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T06:34:30.8414989Z http.https://github.com/.extraheader
2025-03-14T06:34:30.8530103Z A job completed hook has been configured by the self-hosted runner administrator
2025-03-14T06:34:30.8554784Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh'
2025-03-14T06:34:30.8560087Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T06:34:30.8560515Z ##[endgroup]
2025-03-14T06:34:30.8655003Z [!ALERT!] Swap in detected! [!ALERT!]
2025-03-14T06:34:48.2772337Z Cleaning up orphan processes